Senior AI/ML Engineer

Job Summary

We are seeking a highly experienced Senior AI/ML Engineer with 11+ years of experience designing, building, and deploying machine learning and artificial intelligence solutions at scale.

The ideal candidate has deep expertise across the full ML lifecycle — from data engineering and model development to production deployment and MLOps — and has a track record of leading complex AI initiatives in enterprise environments.

Key Responsibilities

• Architect, design, and implement end-to-end machine learning and deep learning solutions for business-critical applications
• Lead the development of predictive models, NLP systems, computer vision pipelines, and/or generative AI/LLM-based applications
• Own the ML lifecycle: data ingestion, feature engineering, model training, validation, deployment, and monitoring
• Build and maintain scalable MLOps pipelines using tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML
• Collaborate with data engineering teams to design robust data pipelines (batch and streaming)
• Optimize model performance, latency, and cost for production-grade systems
• Mentor junior and mid-level ML engineers/data scientists; provide technical leadership across teams
• Partner with product and business stakeholders to translate requirements into AI/ML solutions
• Evaluate and integrate emerging technologies, including LLMs, RAG architectures, and agentic AI frameworks
• Ensure model governance, explainability, fairness, and compliance with data privacy standards
• Drive best practices in code quality, testing, CI/CD, and model versioning

Required Skills & Qualifications

• 11+ years of overall software/ML engineering experience, with 6+ years focused specifically on AI/ML
• Strong programming skills in Python (required); familiarity with Java/Scala/C++ a plus
• Expertise in ML/DL frameworks: TensorFlow, PyTorch, Scikit-learn, Keras
• Hands-on experience with NLP (Transformers, BERT, GPT-based models), Computer Vision, or Generative AI/LLMs
• Strong understanding of MLOps practices: CI/CD for ML, model monitoring, versioning (MLflow, DVC)
• Cloud platform expertise: AWS (SageMaker), Azure (Azure ML), or Google Cloud Platform (Vertex AI)
• Experience with big data tools: Spark, Hadoop, Kafka
• Solid grounding in statistics, algorithms, and data structures
• Experience with vector databases (Pinecone, Weaviate, FAISS) and RAG pipelines is a strong plus
• Proven experience leading ML projects from POC to production
• Excellent communication and stakeholder management skills