Lead AI Engineer – Machine Learning

We are looking for a visionary and technically skilled Lead AI Engineer – Machine Learning to spearhead the design, development, and deployment of advanced machine learning solutions. In this leadership role, you will guide a team of AI/ML engineers, contribute hands-on to critical technical challenges, and collaborate with cross-functional teams to deliver impactful AI products. This role is ideal for someone who thrives at the intersection of innovation, engineering rigor, and business value. Key Responsibilities Technical Leadership : Lead the architecture, development, and deployment of machine learning models and AI systems across a range of use cases. Model Development : Design, train, and optimize supervised, unsupervised, and deep learning models using frameworks like PyTorch, TensorFlow, and XGBoost. Mentorship : Coach and mentor a team of ML engineers and data scientists; foster a culture of innovation, ownership, and continuous learning. Project Management : Drive planning, execution, and delivery of AI/ML projects, ensuring alignment with business objectives and technical feasibility. System Design : Architect scalable, secure, and high-performance ML pipelines and services using cloud-native tools and MLOps best practices. Collaboration : Work closely with product managers, data engineers, and DevOps teams to translate business problems into AI-driven solutions. Code Quality & Governance : Establish standards for model quality, reproducibility, documentation, versioning, and monitoring. Innovation : Stay current with research and industry trends in ML/AI, evaluate new tools, and introduce state-of-the-art solutions where applicable. Required Skills and Experience Education : Bachelor’s or Master’s in Computer Science, Machine Learning, Data Science, or related technical field Experience : 6–10+ years of experience in software engineering or AI/ML, with at least 2+ years in a technical leadership role Technical Expertise : o Strong programming skills in Python and experience with ML libraries such as Scikit-learn, TensorFlow, PyTorch, Hugging Face o Deep understanding of ML fundamentals: feature engineering, model evaluation, optimization, and deployment o Proficiency in designing and building data pipelines, real-time processing, and model inference systems o Experience with cloud platforms (AWS, GCP, or Azure), containerization (Docker, Kubernetes), and CI/CD pipelines o Familiarity with MLOps tools (e.g., MLflow, DVC, Airflow, SageMaker) and vector databases (e.g., FAISS, Pinecone) Preferred Qualifications Hands-on experience with LLMs, RAG pipelines, or generative AI applications Familiarity with agentic AI frameworks (LangChain, CrewAI, AutoGPT) Domain expertise in fintech, healthtech, HR tech, or industrial automation Contributions to open-source AI/ML projects or published research Knowledge of responsible AI practices, explainability (XAI), and model governance Soft Skills Strong leadership and team-building skills Clear and persuasive communication with both technical and non-technical stakeholders Strategic thinker with attention to detail and a bias for action Show more Show less