AI Engineer

Responsibilities & Key Deliverables

We seek AI Engineers who can build and ship production‑grade AI systems (LLMs, NLP, Computer Vision, predictive analytics) across M&M’s businesses. Candidates must demonstrate strong engineering fundamentals, hands‑on model lifecycle ownership, and enterprise deployment experience.

Responsibilities

• Design, train, fine‑tune, and evaluate models; drive A/B experiments and benchmarking.


• Build scalable data and model pipelines (feature extraction, training, inference, monitoring).


• Deploy on cloud (Azure/AWS/GCP) using Docker/Kubernetes; implement CI/CD and observability.


• Integrate models into services/APIs; ensure SLAs on latency, throughput, and cost.


• Implement MLOps best practices (MLflow/model registry/versioning, retraining triggers, drift detection).


• Collaborate with product/business teams; translate requirements into robust AI solutions.

Experience

3–8 years in AI/ML/Applied ML Engineering; minimum 2 production deployments.

Industry Preferred

Qualifications

Bachelor’s/Master’s in CS/EE/Math/AI/related fields (or demonstrably equivalent experience).

General Requirements

• Tech: Python, PyTorch or TensorFlow; cloud (Azure/AWS/GCP); Docker/K8s; CI/CD; MLflow or equivalent.


• GenAI/LLMs: Practical knowledge of prompting, RAG, vector DBs (FAISS/PGVector/Weaviate/Milvus), and fine‑tuning or adapters (LoRA/QLoRA).


• Systems: Ability to profile/optimize inference (batching, quantization).


• Work Mode: WFO only; willingness to relocate to Mumbai preferred.

Soft Skills

• Product mindset and problem‑solving.


• Cross‑functional collaboration and crisp communication.

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