Senior AI/ML Engineer — Applied AI

Applied AI Hyderabad / Hybrid Full-time 59 years

Lead end-to-end delivery of GenAI engagements for global enterprises from problem framing to the production runbook.

The team

ACI Infotech's Applied AI practice (ArqAI) takes enterprise customers from AI strategy to working production systems. Engagements run with a lead architect, a small delivery team, and a customer counterpart across financial services, insurance, healthcare, retail, and manufacturing. Industries where uptime, auditability, and cost are non-negotiable.

What you'll do

Translate a customer problem statement into an applied-AI solution: what to retrieve, what to generate, what to evaluate, and what to leave to a deterministic system.

Design and ship RAG pipelines, agentic workflows, and LLM-powered features deployed inside customer environments and integrated with their data, identity, and observability stack.

Own model selection, prompt strategy, fine-tuning where it earns its keep, evaluation, and the production runbook.

Build the guardrails input filtering, output validation, evals, and observability so the system is safe to operate after we leave.

Lead technical reviews and mentor mid-level engineers on the engagement.

What we're looking for

5+ years in software or ML engineering, with 2+ years building and operating GenAI / LLM systems in production.

Strong Python. Hands-on with LangChain, LlamaIndex, or LangGraph, and a vector database (Pinecone, Weaviate, pgvector, FAISS, or Qdrant).

Production experience on Azure OpenAI, AWS Bedrock, or Vertex AI including the deployment, cost, and security trade-offs.

Working understanding of MLOps: experiment tracking, CI/CD for models, containerization, and observability for LLM applications.

Comfort working with customer engineers and architects. You can run a design review, defend a technical decision, and write things down.

Bonus

Experience in a regulated industry (financial services, healthcare, insurance) and the compliance posture that comes with it.

Familiarity with MLflow, Weights & Biases, or similar tooling.

Background in fine-tuning, distillation, or running smaller open-weight models where the economics make sense.