Job Summary

Synechron is seeking a Senior Agentic AI / Generative AI Engineer to design develop and scale autonomous production-grade AI systems.

The role will focus on building complex multi-agent orchestration frameworks and advanced Retrieval-Augmented Generation (RAG) systems using AWS Bedrock as the primary cloud foundation model platform. The engineer will work with structured and unstructured enterprise datasets to develop autonomous reasoning planning memory and tool-calling workflows that safely automate complex business processes.

The position will contribute to business objectives by enabling intelligent workflow automation improving operational efficiency supporting reliable decision-making 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 frameworks.
- AWS Bedrock: Experience deploying and scaling foundation models including Anthropic Claude Llama and Mistral.
- AWS Bedrock capabilities: Experience with 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 developing ingestion chunking embedding metadata retrieval reranking and evaluation pipelines.
- Vector infrastructure: Solid understanding of semantic search mechanics indexing algorithms vector databases and reranking pipelines.
- Vector databases: Experience with Pinecone OpenSearch pgvector Chroma or equivalent technologies.
- LLMOps: Experience with prompt versioning log streaming latency tracking quality measurement and cost optimization metrics.
- AI safety: Knowledge of security guardrails prompt-injection handling data alignment secure tool execution and responsible AI practices.

Preferred Software Skills

- Contributions to open-source agent libraries or Model Context Protocol (MCP) ecosystems.
- Experience fine-tuning models using PEFT or LoRA techniques for specialized domain knowledge.
- 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 resource-productive and sustainable AI systems.

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 AWS services orchestration frameworks and vector technologies.
- 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 experimental or prompt-based prototypes.

Overall Responsibilities

- Design and orchestrate multi-agent workflows using planners executors retrievers validators and human-in-the-loop patterns.
- Implement reasoning mechanics such as ReAct chain-of-thought tree-of-thought and self-reflection loops with appropriate safety and observability controls.
- Develop robust memory systems using state graphs short-term session logs and long-term knowledge bases.
- 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 enterprise AI solutions.
- Develop secure serverless backend workflows using AWS Lambda and AWS Step Functions.
- 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 refine semantic search engines using vector databases and reranking pipelines.
- Design evaluation datasets and data pipelines to track concept drift data drift response quality and automated system scoring.
- Integrate unstructured enterprise repositories with LLM applications while maintaining semantic indexing accuracy and data protection.
- Translate abstract business processes into deterministic code-driven step logic and measurable technical outcomes.
- Collaborate with product engineering data security and operations teams to define requirements and delivery plans.
- Participate in code reviews provide technical guidance and support team members with 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 proficiency in Python.
- Experience developing 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 retries error handling 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 working with structured and unstructured enterprise datasets.
- 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 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 document-processing data lake and enterprise repository integration patterns.
- Experience with data reconciliation archival lifecycle management and metadata governance.
- Exposure to 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-workplace 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 embeddings retrieval reranking and evaluation.
- Libraries supporting tool calling memory management workflow state API integration and agent observability.
- Experience implementing planning execution delegation validation reflection routing and human-approval patterns.

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 create prototypes and proof-of-concept solutions and transition suitable solutions into production.
- Experience translating 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: 35 years of experience in core software development AI engineering backend engineering or a related technical role.
- Agentic AI experience: At least 12 years exclusively focused on production-level LLM and agentic AI workflows.
- Technical experience: Expert Python 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 with structured and unstructured 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 experience: Ability to translate abstract business processes into deterministic code-driven step logic.
- Industry preference: Experience delivering enterprise regulated operationally critical or data-sensitive AI solutions is preferred.
- Alternative experience pathways: Equivalent combinations of 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 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

- Bachelors or Masters degree in Computer Science Information Technology Engineering Artificial Intelligence or a related field; equivalent practical experience may be considered.
- 35 years of core software development experience including at least 12 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.

SYNECHRONS 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 applicants gender gender identity sexual orientation race ethnicity disabled or veteran status or any other characteristic protected by law.

Candidate Application Notice

Required Experience:

Unclear Seniority

📌 Agentic AI Engineer – AWS Bedrock, LangGraph, RAG & Multi-Agent Systems (Pune)
🏢 Synechron
📍 Pune