AI-Lead Software Engineer

Opteamix; Bengaluru; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

Opteamix is seeking an experienced professional to lead the delivery of enterprise-grade GenAI systems in Bengaluru, India. The ideal candidate will have 2+ years of hands-on experience and a strong grasp of cloud platforms such as AWS, Azure, or GCP.

This role requires solid expertise in Python, multi-agent workflows, and DevOps methodologies, along with the ability to mentor a team of AI engineers.

Join us to work on business-critical AI initiatives and drive innovation!

Qualifications

• 2+ years delivering enterprise-grade GenAI systems in production.
• Experience with AWS, Azure, or GCP, and modern DevOps pipelines.
• End-to-end delivery ownership working with architects and project managers.
• Strong grasp of GenAI evaluation and safety.
• Team leadership of 3+ AI engineers.

Skills

Python

LLM APIs (OpenAI, Azure OpenAI, Anthropic, or Gemini)

RAG architecture

Vector databases (Pinecone, Weaviate, pgvector, or Azure AI Search)

Agentic frameworks (LangChain, LangGraph, CrewAI, or AutoGen)

Docker

Kubernetes

CI/CD

Infrastructure-as-code (Terraform)

FastAPI

Job description

2+ years delivering enterprise-grade GenAI systems in production, including RAG pipelines, multi-agent and agentic workflows, function/tool calling, and MCP-based integrations with enterprise systems. Experience must extend beyond proofs of concept into live, business-critical deployments.

3+ years on a major cloud (AWS / Azure / GCP) and modern DevOps pipelines, including containers, infrastructure-as-code, and CI/CD, with hands‑on ownership of deployment, observability, and cost and latency tuning for AI workloads.

End-to-end delivery ownership, with a track record of working alongside architects and project managers to take AI initiatives from discovery through production rollout and steady‑state operations.

Strong grasp of GenAI evaluation, observability, and safety, with experience building evaluation harnesses, monitoring for hallucinations, tracking cost and latency, and implementing guardrails against prompt injection.

Strong cross‑functional collaboration, having worked alongside security, data engineering, and QA teams, and engaged directly with client SMEs through the delivery lifecycle.

Team leadership of 3+ AI engineers, including mentoring, code reviews, and the ability to translate between business stakeholders and the engineering team.

Must‑have: Python, LLM APIs (OpenAI, Azure OpenAI, Anthropic, or Gemini), RAG architecture (chunking, embeddings, re‑ranking), vector databases (Pinecone, Weaviate, pgvector, or Azure AI Search), agentic frameworks (LangChain, LangGraph, CrewAI, or AutoGen), MCP and function/tool calling, prompt engineering, LLM evaluation tooling (Ragas, LangSmith, or custom harnesses), at least one major cloud (AWS, Azure, or GCP), Docker, Kubernetes, CI/CD, infrastructure‑as‑code (Terraform), FastAPI and microservices.

Good to have: Fine‑tuning (LoRA or QLoRA), open‑source LLMs (Llama, Mistral, or Qwen), voice and vision agents, GraphRAG and knowledge graphs (Neo4j).