AI / ML Engineer

About This Role

We're building AI products for enterprise clients across fintech, healthcare, and logistics, and we need engineers who can take an AI idea from whiteboard to production. You'll design and train ML models, build LLM-powered pipelines using LangChain and LlamaIndex, and work closely with clients to turn vague AI requirements into systems that actually work. This role spans the full ML lifecycle — from data exploration to model serving to monitoring in production.

Responsibilities

• Design, train, and deploy machine learning models for client use cases including NLP, computer vision, and predictive analytics
• Build production-grade LLM pipelines using LangChain, LlamaIndex, or custom orchestration
• Implement RAG (Retrieval-Augmented Generation) systems with vector databases (Pinecone, Weaviate, pgvector)
• Fine-tune open-source models (LLaMA, Mistral, Gemma) for domain-specific tasks
• Set up MLOps pipelines for model versioning, experiment tracking, and A/B testing (MLflow, Weights & Biases)
• Build data pipelines to transform raw client data into training-ready formats
• Monitor model drift and implement retraining triggers in production systems
• Write clear technical documentation for model architecture and API contracts

Requirements

• 3+ years working in ML/AI engineering — not just research, but actual production deployments
• Strong Python skills: NumPy, Pandas, scikit-learn, PyTorch or TensorFlow
• Hands-on experience with LLM APIs (OpenAI, Anthropic, Gemini) and open-source models
• Understanding of vector search, embeddings, and semantic similarity
• Experience with REST API development for ML model serving (FastAPI, Flask)
• Familiarity with cloud ML services: AWS SageMaker, GCP Vertex AI, or Azure ML
• Ability to communicate model results and limitations clearly to non-technical stakeholders

Nice to Have

Bonus points — not required

• Published papers or competition placements (Kaggle, etc.)
• Experience with multi-modal models (vision-language, image generation)
• Knowledge of reinforcement learning from human feedback (RLHF) and model alignment
• Experience building AI agents with tool use and multi-step reasoning