Senior Machine Learning Engineer

Job Summary

We are seeking a Lead/Sr ML/AI Engineer to design, deploy, and optimize production‑grade ML and GenAI systems across all HiLabs products. You will convert research prototypes into highly available, secure, and scalable services on AWS while driving engineering best practices throughout the full model and data lifecycle.

Key Responsibilities

• End‑to‑End ML & AI Engineering
• Be the Ops lead to support DS team in the full ML/AI lifecycle—data preparation, feature engineering, model training, evaluation, deployment, and real‑time inference (MLFlow, DVC, Git, Grafana, PagerDuty).
• Productionize classical ML, deep‑learning, and GenAI/LLM applications (EKS, Docker, Kafka, FastAPI, Poetry).
• Implement sophisticated RAG pipelines and prompt‑engineering frameworks for LLM‑driven applications (LangChain, LangGraph, MCP, LlamaIndex, HuggingFace).
• Data & Pipeline Architecture
• Design, build, and automate data ingestion, quality checks, feature stores, and graph/data‑versioning systems (Airflow).
• Establish robust governance—lineage, security, HIPAA compliance, and fine‑grained access controls.
• MLOps & AIOps
• Create CI/CD + continuous testing (CT) pipelines for both data and models; enable blue‑green/canary rollouts and shadow deployments.
• Instrument automated monitoring for performance, drift, bias, hallucination detection, and cost optimization; trigger auto‑retraining or auto-rollback where needed.
• Software Engineering Excellence
• Write modular, reusable, and well‑documented code
• Champion cloud‑native design—containerization (Docker), orchestration (Kubernetes/EKS), serverless (Lambda), and infrastructure‑as‑code (Terraform/CloudFormation).
• Support architecture reviews, threat modeling, and performance tuning sessions.
• Technical Leadership & Collaboration
• Mentor and grow a team of MLEs, AI engineers, and data engineers; foster a culture of knowledge sharing and innovation.
• Work hand‑in‑hand with data scientists, engineers, product owners/managers to translate complex healthcare problems into reliable AI services.
• Evaluate emerging frameworks and drive their adoption where they add value.

Desired Profile

• Education: Bachelor’s or Master’s in Computer Science, Engineering, Mathematics, or a related quantitative field from Tier‑1 institutions.
• Experience: 5–8 years building and operating production ML/AI systems, including 2+ years in a senior or lead capacity.
• Technical Expertise:
• Strong proficiency in Python (FastAPI, Pydantic, pytest), Spark/Scala, SQL, and one deep‑learning framework (HuggingFace, TensorFlow, PyTorch).
• Hands‑on AWS stack (SageMaker, EKS/ECS, Bedrock, S3, Step Functions, CloudWatch). Azure/GCP a plus.
• Deep knowledge of LLM fine‑tuning, prompt engineering, vector databases (Pinecone, FAISS, OpenSearch), and semantic search.
• Experience with GPU acceleration, distributed training (DeepSpeed), and model compression (quantization, distillation).
• Strong command of DevOps/MLOps tools—Git, Bitbucket Pipelines, Jenkins, MLflow.
• Familiarity with security best practices, including network isolation, secret management, and data encryption at rest/in transit.
• Soft Skills:
• Proven leadership, mentorship, and cross‑functional communication abilities.
• Strong analytical and problem‑solving mindset; able to balance scientific rigor with engineering pragmatism.
• Passion for improving healthcare outcomes through data and AI.