
Lead Artificial Intelligence Engineer
8 hours ago
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
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