Senior AIML Engineer

Core AI/ML Development

• Partner with business, product, and engineering teams to define problem statements, evaluate feasibility, and design AI/ML-driven solutions that deliver measurable business value
• Lead and execute end-to-end AI/ML projects — from data exploration and model development to validation, deployment, and monitoring in production
• Independently design and implement scalable machine learning solutions and data systems, ensuring end-to-end workflows, large-scale analytics, and reliability

Generative AI & LLM Implementation

• Design and implement RAG (Retrieval Augmented Generation) systems for enterprise knowledge management
• Develop guardrails and safety measures for GenAI applications in production
• Implement cost optimization strategies for LLM inference at scale
• Create synthetic data generation pipelines for model training and testing
• Build and optimize prompt engineering strategies and fine-tuning pipelines

Traditional ML Excellence

• Drive solution architecture using techniques in data engineering, programming, machine learning, NLP, and computer vision
• Implement and refine feature engineering, monitoring, ML pipelines, deploy models in production
• Build real-time inference APIs with sub-second latency requirements
• Develop forecasting models for demand prediction and supply chain optimization
• Create recommendation systems for route optimization and customer solutions

MLOps & Production Engineering

• Champion the scalability, reproducibility, and sustainability of AI solutions by establishing best practices in model development, CI/CD, and performance tracking
• Ensure readiness for production releases, focusing on testing, monitoring, observability, and maintaining scalability
• Implement comprehensive model versioning, registry, and rollback strategies
• Build automated retraining pipelines and drift detection systems

Leadership & Collaboration

• Guide junior and associate AI/ML engineers through technical mentoring, code reviews, and solution reviews
• Translate technical outputs into actionable insights for business stakeholders through storytelling and data visualizations
• Drive cross-team and cross-discipline initiatives to optimize workflows and enhance collaboration
• Identify and evangelize the adoption of emerging tools, technologies, and methodologies across teams

Technical Requirements

Essential Skills

Programming & Data Engineering:

• Advanced proficiency in Python, SQL, PySpark
• Experience with Docker, Kubernetes for containerization
• Strong software engineering practices (clean code, testing, documentation)

Cloud & Infrastructure (Azure preferred):

• Databricks, Azure ML, ADF, Web Apps
• Experience with distributed computing and big data processing
• Infrastructure as Code (Terraform, ARM templates)

LLM/Generative AI Stack:

• Hands-on experience with foundation models: GPT-4, Claude, Gemini
• LLM frameworks: LangChain, LlamaIndex, LangGraph
• Vector databases: Pinecone, Chroma, pgvector
• Fine-tuning techniques: LoRA, QLoRA, PEFT
• Hugging Face ecosystem (Transformers, Datasets, Hub)
• Embedding models and semantic search implementation

Traditional ML/Deep Learning:

• Deep learning frameworks: TensorFlow, PyTorch, JAX
• Classical ML: scikit-learn, XGBoost, LightGBM, Regression and Classification
• Strong expertise in NLP, Time Series Forecasting
• Experience with recommendation systems and reinforcement learning
• Solid understanding of model evaluation, optimization, bias mitigation, and monitoring.

MLOps & Monitoring:

• MLflow, Weights & Biases for experiment tracking