Principal AI/ML Architect

Principal AI-ML Architect

Role Overview

We are looking for an AI/ML Architect with strong hands‑on experience in Python‑based ML systems and GenAI solutions. This role focuses on designing and deploying production‑grade AI systems, especially leveraging LLMs, RAG pipelines, and MLOps practices.

You will work closely with engineering and product teams to build scalable, secure, and efficient AI‑powered applications.

Key Responsibilities

• AI/ML System Design

  • Design and implement end‑to‑end ML pipelines (data ingestion training evaluation deployment).
  • Architect LLM‑based solutions using advanced prompting strategies, RAG (Retrieval‑Augmented Generation) and agentic workflows.
  • Define scalable patterns for ML/ GenAI application development.

• Model Development & Optimization

  • Work on data analysis, quality benchmarking, lineage detection and curation, ingestion into vector stores
  • Work on statistical model training, evaluation, hyper‑parameter tuning, feature engineering
  • Work on fine‑tuning of LLMs for specific tasks and prompt optimization (no expectation to build models from scratch at large scale).
  • Evaluate and select appropriate models (open weights or closed weights).
  • Collaborate with data teams for feature engineering and dataset readiness.

• MLOps & Deployment

  • Implement MLOps best practices:
    • Model versioning
    • Experiment tracking
    • Monitoring & retraining pipelines
    • Prompt versioning
    • Drift detection
    • Token costs
  • Handle model deployment in production environments (APIs, batch, streaming).
  • Ensure performance, scalability, and reliability of AI systems.

• Platform & Integration

  • Integrate AI solutions with existing microservices and backend systems.
  • Work with vector databases, caching, and APIs for GenAI use cases.
  • Ensure security and governance in AI deployments.

• Collaboration

  • Partner with product managers and engineers to translate business problems into AI solutions.
  • Mentor engineers on AI/ML and GenAI best practices.

Must Have Skills

• Core

  • 10–15 years of experience in software engineering / ML systems.
  • Strong programming skills in Python (mandatory).
  • Experience in building production‑grade ML systems (not just notebooks).

• AI/ML & GenAI

  • Hands‑on experience with:
    • Data Analysis and curation
    • Feature engineering
    • Statistical model training, evaluation & hyper parameter tuning
    • LLMs / GenAI applications
    • RAG pipeline design
    • Prompt engineering & model tuning
  • Experience with frameworks like Tensorflow, PyTorch, Sci‑kit, LangChain, LlamaIndex, or similar.
  • Understanding of embeddings, vector search, and retrieval systems.
  • Exposure to custom model fine‑tuning (good to have, not mandatory).

• MLOps & Deployment

  • Experience with:
    • Model deployment (API‑based or batch)
    • CI/CD pipelines for ML
    • Monitoring and logging
  • Familiarity with tools like MLflow, Kubeflow, or similar (any one is fine).

• Cloud & Scalability

  • Experience with at least one cloud: AWS / Azure / GCP.
  • Understanding of scalable system design and APIs.

• Data & Systems

  • Working knowledge of databases (SQL/NoSQL).
  • Experience with vector databases (Milvus, Pinecone, Weaviate, FAISS, etc.).

Good to Have (Optional)

• Experience in AIOps or AI for observability/use‑case automation.
• Background in data engineering or analytics pipelines.
• Exposure to Kubernetes/Docker.
• Experience in telecom or high‑scale product environments.

Location

Hyderabad / Bangalore (Work from office