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: Strong 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-efficient, 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…