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 : Bachelors or Masters in Computer Science, Machine Learning, Data Science, or related technical field

- Experience : 610+ years of experience in software engineering or AI/ML, with at least 2+ years in a technical leadership role

Technical Expertise :

- Strong programming skills in Python and experience with ML libraries such as Scikit-learn, TensorFlow, PyTorch, Hugging Face

- Deep understanding of ML fundamentals : feature engineering, model evaluation, optimization, and deployment

- Proficiency in designing and building data pipelines, real-time processing, and model inference systems

- Experience with cloud platforms (AWS, GCP, or Azure), containerization (Docker, Kubernetes), and CI/CD pipelines

- 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