Job Summary
Synechron is seeking a Agentic AI / Generative AI Engineer to design, develop, and scale autonomous, production-grade AI systems.
The role will focus on developing complex multi-agent orchestration frameworks and advanced Retrieval-Augmented Generation (RAG) systems using AWS Bedrock as the primary foundation model platform. The successful candidate will work with structured and unstructured enterprise data to build autonomous reasoning, planning, memory, and tool-calling workflows that safely automate complex business processes.
This position will contribute to business objectives by enabling intelligent workflow automation, improving decision support, increasing operational efficiency, and delivering secure, scalable, measurable, and maintainable AI solutions.
Software Requirements
Required Software Skills
Python: Expert proficiency for AI engineering, backend services, data pipelines, agent workflows, and production applications.
Asynchronous backend APIs: Hands-on experience with FastAPI, Flask, or equivalent Python API frameworks.
AWS Bedrock: Experience deploying and scaling foundation models, including model access, Knowledge Bases, Agents, Guardrails, and custom model evaluations.
AWS services: Hands-on experience with EC2, Lambda, IAM, VPCs, and Step Functions.
Agent orchestration frameworks: Deep experience with LangGraph, CrewAI, AutoGen, Semantic Kernel, or comparable frameworks.
Multi-agent systems: Experience designing planners, executors, retrievers, validators, human-in-the-loop workflows, and tool-calling loops.
RAG architectures: Experience designing advanced Retrieval-Augmented Generation systems, including ingestion, chunking, embeddings, metadata tagging, retrieval, reranking, and evaluation.
Vector infrastructure: Strong understanding of semantic search, indexing algorithms, vector databases, and reranking pipelines.
Vector databases: Experience with Pinecone, OpenSearch, pgvector, Chroma, or comparable vector technologies.
LLMOps: Experience with prompt versioning, log streaming, latency tracking, cost monitoring, evaluation, and operational metrics.
AI safety: Knowledge of security guardrails, prompt-injection prevention, data alignment, access controls, and safe tool execution.
Data pipelines: Experience developing pipelines for structured and unstructured enterprise data, embedding generation, semantic indexing, and evaluation datasets.
Preferred Software Skills
Experience with Model Context Protocol (MCP) ecosystems or contributions to open-source agent libraries.
Experience fine-tuning models using PEFT or LoRA techniques.
Familiarity with model monitoring, concept drift, data drift, automated scoring, and evaluation operations.
Experience with infrastructure as code, containerization, CI/CD, and automated deployment.
Experience designing cost-efficient and resource-efficient AI systems.
Exposure to additional AI, data engineering, or cloud-native technologies relevant to production GenAI applications.
Version and Experience Guidance
Specific software versions are not mandated in the requirements.
Candidates should have experience with current, production-supported versions of Python, backend frameworks, agent orchestration frameworks, vector technologies, and AWS services.
Candidates should be able to assess compatibility, manage upgrades, maintain version control, and support production lifecycle management.
Experience should include production-level LLM and agentic AI workflows rather than only experimentation or prompt-based prototypes.
Overall Responsibilities
Design and orchestrate multi-agent workflows using planners, executors, retrievers, validators, and human-in-the-loop patterns.
Implement advanced reasoning mechanics, including ReAct, chain-of-thought, tree-of-thought, and self-reflection loops, with appropriate safety and observability controls.
Develop memory systems using state graphs, short-term session logs, and long-term knowledge bases to maintain state and context across multi-step workflows.
Build autonomous task-execution engines with fine-grained tool calling, API integrations, fallback strategies, validation, and escalation controls.
Deploy and scale foundation models through AWS Bedrock.
Use AWS Bedrock Knowledge Bases, Agents, Guardrails, and custom model evaluations to develop reliable enterprise AI solutions.
Develop secure serverless workflows using AWS Lambda and AWS Step Functions to trigger and coordinate agent actions.
Implement LLMOps practices for prompt versioning, log streaming, latency tracking, quality measurement, and cost optimization.
Architect end-to-end data-ingestion pipelines for embedding generation, chunking strategies, metadata tagging, and semantic indexing.
Develop and fine-tune semantic search systems using vector databases and reranking pipelines.
Design evaluation datasets and data pipelines to track concept drift, data drift, response quality, and automated system scores.
Integrate unstructured enterprise repositories with LLM applications while maintaining semantic indexing accuracy, data protection, and access controls.
Translate abstract business processes into deterministic, code-driven workflows and measurable technical outcomes.
Collaborate with product, engineering, data, security, and operational stakeholders to define requirements and delivery plans.
Review code and architecture, support technical decision-making, and guide team members through implementation and troubleshooting.
Optimize AI systems for reliability, safety, scalability, latency, maintainability, sustainability, and cost.
Maintain technical documentation covering architecture, workflows, prompts, data pipelines, evaluation methods, operating procedures, and known limitations.
Technical Skills (By Category)
Programming Languages
Essential
Expert-level Python development experience.
Experience building asynchronous backend APIs using FastAPI, Flask, or equivalent frameworks.
Ability to write modular, maintainable, testable, and production-ready code.
Ability to develop agent tools, API integrations, data pipelines, evaluation utilities, and automation components.
Understanding of asynchronous execution, error handling, retries, logging, and workflow state management.
Preferred
Experience with additional programming languages used in cloud-native, data, or enterprise application development.
Experience developing reusable libraries and shared components for AI applications.
Exposure to event-driven and distributed application development.
Databases/Data Management
Essential
Experience managing structured and unstructured enterprise data for AI applications.
Strong understanding of embeddings, chunking, metadata tagging, semantic indexing, retrieval, and reranking.
Experience with vector databases such as Pinecone, OpenSearch, pgvector, Chroma, or equivalent technologies.
Understanding of semantic search mechanics and indexing algorithms.
Ability to design data-ingestion pipelines for RAG applications.
Experience creating evaluation datasets and data pipelines for automated scoring, data drift, and concept drift analysis.
Understanding of data quality, lineage, access control, retention, and data alignment.
Preferred
Experience with NoSQL databases and knowledge-base technologies.
Familiarity with data lake, document-processing, and enterprise repository integration patterns.
Experience with data reconciliation, archival, lifecycle management, and metadata governance.
Exposure to specialized data preparation for model fine-tuning using PEFT or LoRA.
Cloud Technologies
Essential
Hands-on experience with AWS Bedrock.
Experience with AWS EC2, Lambda, IAM, VPCs, and Step Functions.
Understanding of secure, scalable, and highly available cloud architecture.
Experience designing serverless workflows for agent execution and orchestration.
Understanding of cloud identity, networking, service integration, monitoring, and operational support.
Ability to optimize AI workloads for latency, reliability, resource utilization, and cost.
Preferred
Experience with cloud-native deployment automation and infrastructure as code.
Familiarity with containerization and Kubernetes-based deployments.
Experience with multi-environment or hybrid cloud application delivery.
Understanding of sustainable cloud operations and efficient resource allocation.
Frameworks and Libraries
Essential
LangGraph, CrewAI, AutoGen, Semantic Kernel, or comparable agent orchestration frameworks.
AWS Bedrock Agents, Knowledge Bases, Guardrails, and model evaluation capabilities.
FastAPI, Flask, or equivalent asynchronous backend API frameworks.
Vector database and semantic search technologies.
Frameworks and libraries for embedding generation, retrieval, reranking, and evaluation.
Libraries supporting tool calling, memory management, workflow state, API integration, and agent observability.
Experience implementing multi-agent patterns such as planning, execution, delegation, validation, reflection, routing, and human approval.
Preferred
Model Context Protocol (MCP) libraries and ecosystem integrations.
Model-serving, LLMOps, evaluation, and monitoring frameworks.
Fine-tuning libraries supporting PEFT and LoRA techniques.
Additional RAG, document-processing, and knowledge-management frameworks.
Development Tools and Methodologies
Essential
Experience across the AI application lifecycle, including requirements analysis, architecture, development, testing, deployment, monitoring, and support.
Experience developing production-level LLM and agentic AI workflows.
Experience with Git, code reviews, technical documentation, automated testing, and release practices.
Ability to define technical approaches, architecture, delivery plans, quality standards, and evaluation criteria.
Experience implementing LLMOps practices, including prompt versioning, log streaming, latency measurement, quality evaluation, and cost tracking.
Ability to build prototypes and proof-of-concept solutions and transition suitable solutions into production.
Experience translating abstract business processes into deterministic, code-driven workflow logic.
Familiarity with Agile delivery, sprint planning, daily stand-ups, reviews, and retrospectives.
Preferred
Experience with CI/CD pipelines, infrastructure as code, containers, and automated deployment.
Familiarity with experiment tracking, model monitoring, automated regression evaluation, and production alerting.
Experience with observability, distributed tracing, operational dashboards, and incident response.
Contributions to open-source agent libraries or MCP ecosystems.
Security Protocols
Essential
Knowledge of implementing AI security guardrails and safe tool-calling controls.
Understanding of prompt-injection risks, data leakage, unauthorized tool use, unsafe outputs, and excessive agent autonomy.
Ability to apply data-access controls, identity management, secrets protection, and least-privilege permissions.
Understanding of secure API communication, input validation, output validation, and controlled execution.
Experience checking data alignment, retrieval accuracy, source relevance, and response safety.
Ability to apply responsible AI principles throughout the design, development, evaluation, and deployment lifecycle.
Preferred
Experience with threat modeling for AI agents and LLM applications.
Familiarity with privacy-preserving AI, model security, auditability, and compliance controls.
Experience implementing human approval, escalation, and review processes for higher-risk actions.
Knowledge of security validation within LLMOps, CI/CD, and cloud deployment workflows.
Experience Requirements
Overall experience: 3–5+ years of experience in core software development, AI engineering, backend engineering, or a related technical role.
Agentic AI experience: At least 1–2 years of focused experience developing production-level LLM and agentic AI workflows.
Technical experience: Expert Python development experience with asynchronous APIs, agent orchestration, AWS Bedrock, RAG, vector infrastructure, and cloud services.
Architecture experience: Experience designing multi-agent systems, autonomous task execution engines, memory systems, tool-calling workflows, and secure API integrations.
Data experience: Experience working with structured and unstructured enterprise data, ingestion pipelines, embeddings, semantic search, metadata, evaluation datasets, and data-quality controls.
Safety experience: Knowledge of security guardrails, prompt-injection handling, data alignment, access control, and responsible AI practices.
Domain preference: Experience translating complex business processes into deterministic technical workflows is preferred.
Industry preference: Experience delivering enterprise, regulated, operationally critical, or data-sensitive AI solutions is preferred.
Alternative experience pathways: Equivalent combinations of relevant professional experience, technical training, open-source contributions, substantial AI projects, research, or demonstrable production implementations may be considered.
Outcome evidence: Candidates should demonstrate contributions to reliable production AI systems, improved retrieval quality, safer agent execution, reduced workflow effort, improved latency, or optimized AI operating costs.
Day-to-Day Activities
Design, develop, test, deploy, and optimize multi-agent workflows, RAG pipelines, memory systems, tool integrations, serverless actions, and evaluation processes.
Participate in daily stand-ups, project planning, architecture discussions, technical reviews, code reviews, and cross-functional meetings to confirm requirements and delivery progress.
Deliver production-quality Python code, data pipelines, prompts, evaluation datasets, monitoring configurations, technical documentation, and operating procedures.
Make technical recommendations within agreed architecture, security, and delivery standards while resolving issues and escalating risks related to quality, safety, cost, performance, and reliability.
Qualifications
Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, Artificial Intelligence, or a related field; equivalent practical experience may be considered.
3–5+ years of core software development experience, including at least 1–2 years focused on production-level LLM and agentic AI workflows.
Demonstrated experience with Python, asynchronous backend APIs, AWS Bedrock, agent orchestration frameworks, RAG architectures, vector databases, and cloud services.
AWS Certified Machine Learning – Specialty or AWS Certified Solutions Architect certification is preferred.
Commitment to continuous professional development through training, technical experimentation, open-source contribution, knowledge sharing, and ongoing learning in agentic AI, LLMOps, cloud engineering, data systems, and responsible AI.
Professional Competencies
Applies critical thinking to decompose complex business processes into deterministic workflows, evaluate technical trade-offs, and resolve AI engineering challenges.
Collaborates effectively with product, engineering, data, security, operations, and delivery teams while supporting technical guidance and knowledge sharing.
Communicates architecture, AI behavior, data requirements, technical risks, evaluation results, and business impact clearly to technical and non-technical stakeholders.
Adapts to evolving models, frameworks, cloud capabilities, data requirements, security risks, and delivery priorities through continuous learning.
Evaluates emerging technologies using evidence, prototypes, measurable criteria, and responsible AI principles before recommending adoption.
Manages priorities effectively across development, experimentation, testing, deployment, support, documentation, and continuous improvement while maintaining focus on secure, reliable, ethical, and maintainable outcomes.
SYNECHRON’S DIVERSITY & INCLUSION STATEMENT
Diversity & Inclusion are fundamental to our culture, and Synechron is proud to be an equal opportunity workplace and is an affirmative action employer. Our Diversity, Equity, and Inclusion (DEI) initiative ‘Same Difference’ is committed to fostering an inclusive culture – promoting equality, diversity and an environment that is respectful to all. We strongly believe that a diverse workforce helps build stronger, successful businesses as a global company. We encourage applicants from across diverse backgrounds, race, ethnicities, religion, age, marital status, gender, sexual orientations, or disabilities to apply. We empower our global workforce by offering flexible workplace arrangements, mentoring, internal mobility, learning and development programs, and more.
All employment decisions at Synechron are based on business needs, job requirements and individual qualifications, without regard to the applicant’s gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law.
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