Sr. Agentic AI Engineer

SynapOne; Bengaluru; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

SynapOne is seeking a hands-on Agentic AI Engineer to build and deploy production-grade AI solutions. You will own the full build lifecycle, from prototyping to production, collaborating with senior architects and data scientists in a consulting environment.

You will develop LLM-based applications, implement RAG pipelines, manage embeddings, and containerize services for cloud deployment. Strong Python, MLOps, and cloud experience are required.

Qualifications

• 2–5 years in software engineering, data engineering, or ML; at least 1 year building LLM/Gen AI applications.
• Strong Python skills — OOP, async programming, packaging, and testing.
• Hands-on experience with LangChain, LangGraph, CrewAI, AutoGen, or LlamaIndex.
• Working knowledge of LLM APIs (OpenAI, Anthropic Claude, Gemini) and prompt design.
• Familiarity with AWS or Azure; comfortable with REST APIs and Git-based workflows.
• Hands-on experience deploying, monitoring, and maintaining AI systems in production — Docker/Kubernetes, CI/CD pipelines, and observability tooling are non-negotiable.
• Solid SQL skills, API design experience (FastAPI/Flask), and a software-engineering-first approach to ML.
• B.Tech / B.E. / M.Sc. in Computer Science, Information Technology, or a related field

Responsibilities

• Build LLM-based applications and multi-step agentic workflows.
• Implement Retrieval-Augmented Generation pipelines with chunking, embeddings, and vector search.
• Create agents with tool-calling, memory, and human-in-the-loop checkpoints.
• Design and iterate on prompts, system prompts, chain-of-thought templates, and structured outputs.
• Expose AI capabilities via FastAPI endpoints; integrate with client data sources and APIs.
• Manage embeddings with vector databases like Pinecone, Qdrant, or pgvector.
• Containerize and deploy AI services using Docker/Kubernetes on cloud environments; ensure high availability and low latency.
• Implement observability for AI systems using LangSmith or Arize Phoenix; monitor accuracy, hallucinations, cost, latency.
• Maintain CI/CD pipelines for ML with automated testing (unit, contract, model-quality).
• Apply output validation, guardrails, and hallucination-detection techniques for production-safe outputs.
• Collaborate with senior architects and engagement managers to translate requirements into technical tasks.
• Write clean, modular Python; participate in reviews and contribute to reusable libraries.

Skills

Python

LLM Frameworks

Gen AI

MLOps

REST APIs

SQL

GPU/Cloud basics

Code quality

Education

B.Tech / B.E. / M.Sc. in Computer Science or related field

Tools

Docker

Kubernetes

FastAPI

LangChain

Job description

We're looking for a hands-on Agentic AI Engineer to build, optimize, and deploy production-grade AI solutions. In this role, you will be the engine room of our AI initiatives — taking architectural blueprints and turning them into scalable, functional systems. You will work across the full build lifecycle — from prototyping to production — collaborating with Senior Architects and data scientists to deliver AI solutions in a consulting environment.

WHAT YOU'LL DO

Agentic Development

• Build: Develop LLM-based applications and multi-step agentic workflows using frameworks such as Microsoft Agent Framework, AutoGen, LangChain, LangGraph, LlamaIndex, or CrewAI
• RAG Pipelines: Implement Retrieval-Augmented Generation pipelines: chunking, embedding, vector search, and re-ranking
• Tool Use & Memory: Build agents with tool-calling, short/long-term memory, and human-in-the-loop checkpoints
• Prompt Engineering: Design and iterate on system prompts, chain-of-thought templates, and structured output schemas

Model Fine-tuning: Execute fine-tuning and optimization tasks (Quantization, PEFT/LoRA) to adapt foundation models for specific domain tasks

Integration & Delivery

• APIs: Expose AI capabilities via FastAPI endpoints; integrate with client data sources and third-party APIs
• Vector Databases: Manage embeddings and retrieval using Pinecone, Qdrant, or pgvector
• Deployment: Containerize and deploy AI services using Docker and Kubernetes on AWS / Azure cloud environments, ensuring high availability and low latency
• Observability: Implement observability for AI systems using LangSmith or Arize Phoenix, tracking accuracy, hallucinations, cost, and latency
• CI/CD: Maintain CI/CD pipelines for ML, ensuring automated testing (unit, contract, and model-quality tests) is integrated into the delivery workflow
• Guardrails & Hallucination Control: Apply output validation, guardrails, and hallucination-detection techniques to ensure reliable, production-safe AI outputs

Token Optimization: Apply prompt compression, context window management, and response caching to control inference cost and latency Collaboration

• Client Delivery: Work closely with Senior Architects and Engagement Managers to translate business requirements into technical tasks and working solutions
• Code Quality: Write clean, modular Python; participate in peer code reviews and contribute to the team’s internal library of reusable AI patterns and playbooks

MUST-HAVE QUALIFICATIONS

• Experience: 2–5 years in software engineering, data engineering, or ML; at least 1 year building LLM/Gen AI applications
• Python: Strong Python skills — OOP, async programming, packaging, and testing
• LLM Frameworks: Hands-on experience with at least one of: LangChain, LangGraph, CrewAI, AutoGen, or LlamaIndex
• Gen AI: Working knowledge of LLM APIs ("OpenAI, Anthropic Claude, Gemini") and prompt design
• Cloud Basics: Familiarity with AWS or Azure; comfortable with REST APIs and Git-based workflows
• MLOps & Productionization: Hands-on experience deploying, monitoring, and maintaining AI systems in production — Docker/Kubernetes, CI/CD pipelines, and observability tooling are non-negotiable
• Engineering Fundamentals: Solid SQL skills, API design experience (FastAPI/Flask), and a “software engineering first” approach to ML — encompassing testing, modularity, and documentation
• Education: B.Tech / B.E. / M.Sc. in Computer Science, Information Technology, or a related field

GOOD TO HAVE

• Evaluation: Exposure to G-Eval, RAGAS, TruLens, or LangSmith for quantifying LLM output quality
• MLOps: Basic experience with MLflow or DVC for experiment tracking
• Structured Outputs: Experience with Pydantic-based output parsing and function/tool calling
• Performance Tuning: Knowledge of vLLM or Triton Inference Server for high-throughput model serving
• Certifications: AWS / Azure AI Fundamentals or equivalent cloud certification