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.

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).