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…