AI/ML Engineer

We are seeking a highly experienced AI/ML Engineer with strong Python expertise to drive the design and deployment of scalable machine learning and AI systems. In this role, you will architect end-to-end AI/ML solutions, combining classical machine learning, deep learning, and modern LLM-based approaches to solve complex business problems.

You will go beyond experimentation and prototype development to build production-grade AI systems that are reliable, scalable, and continuously improving through feedback loops and monitoring.

The ideal candidate brings deep expertise in ML system design, strong engineering discipline, and the ability to integrate cutting-edge AI capabilities (including LLMs and Agentic systems) into enterprise-grade applications.

Key Responsibilities End-to-End ML System Development

Design, develop, and deploy scalable ML models and pipelines—from data ingestion and feature engineering to model training, evaluation, and production deployment.

Advanced AI Solutioning

Apply a combination of classical ML, deep learning, and LLM-based techniques to solve structured and unstructured data problems across domains.

LLM & Agentic Integration

Build and integrate LLM-powered features, including RAG pipelines, intelligent agents, and workflow automation using modern frameworks.

Model Lifecycle Management (MLOps)

Establish robust MLOps practices including versioning, CI/CD for models, continuous training, monitoring, and drift detection.

Technical Requirements Core ML Expertise

Strong experience across supervised, unsupervised, and deep learning techniques with hands-on implementation in production environments.

Programming & Frameworks

• Expert-level Python (NumPy, Pandas, Scikit-learn)
• Deep learning frameworks: TensorFlow / PyTorch
• Experience in building APIs (FastAPI/Flask)

LLMs & Modern AI Stack

• Hands-on experience with LLM frameworks (LangChain, LlamaIndex)
• Understanding of prompt engineering, RAG, and vector search systems
• Familiarity with OpenAI, Gemini, Anthropic models or open-source alternatives

Data & Infrastructure

• Experience with big data tools (Spark, Hadoop)
• Strong SQL skills and working knowledge of data warehouses
• Experience with vector databases (Pinecone, Weaviate, Milvus, Chroma)

MLOps & Deployment

• Experience with Docker, Kubernetes, CI/CD pipelines
• Model serving frameworks (MLflow, SageMaker, Vertex AI, etc.)
• Monitoring tools for model performance and drift

Software Engineering Best Practices

• Strong understanding of system design, scalability, and clean coding principles
• Experience building production-ready systems (not just prototypes)

Preferred Qualifications

• Experience with Agentic AI systems and orchestration frameworks (LangGraph, CrewAI, AutoGen)
• Knowledge of fine-tuning techniques (LoRA, QLoRA, transfer learning)
• Experience designing real-time ML systems or recommendation engines
• Exposure to multi-modal models (text, image, audio)
• Contributions to open-source projects or strong GitHub portfolio
• Experience mentoring junior engineers and leading technical initiatives