Advanced Machine Learning Engineer

Senior AI Technical Lead

We are seeking an exceptional technical leader to spearhead the design, development, and deployment of advanced machine learning solutions.

This leadership role requires guiding a team of AI / ML engineers, contributing hands-on to critical technical challenges, and collaborating with cross-functional teams to deliver impactful AI products.

Key responsibilities include :

• Technical Leadership : Lead the architecture, development, and deployment of machine learning models and AI systems across various 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 qualifications include :

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 :

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 include hands-on experience with LLMs, RAG pipelines, or generative AI applications, familiarity with agentic AI frameworks, domain expertise in fintech, healthtech, HR tech, or industrial automation, contributions to open-source AI / ML projects or published research, and knowledge of responsible AI practices, explainability (XAI), and model governance.

Additionally, strong leadership and team-building skills, clear and persuasive communication with both technical and non-technical stakeholders, strategic thinking with attention to detail, and a bias for action are required.

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