Sr AI Engineer
Posted Updated
Description
Top 5 skills:
1. Production Experience with atleast 2 Agentic deployments
2. Deep understanding of harness engineering, grounding and evaluation
3. Strong python programming and pydyantic/ Langraph framework
4. LLM Ops, pipelines
5. Clear and strong verbal communication
Strategic Leadership & Initiative Ownership
- Own the end-to-end lifecycle for new AI/ML initiatives, from initial business problem identification through architectural design, development, and eventual deployment.
- Define and execute the AI technology roadmap, constantly evaluating emerging technologies (e.g., LLMs, GenAI, Edge AI) and determining their strategic fit within the organization.
- Mentor and provide technical leadership to junior and mid-level Data Scientists and ML Engineers, fostering a culture of technical excellence and rapid experimentation.
- Serve as the principal technical authority for AI initiatives, communicating vision and progress to executive stakeholders.
MLOps and Production Readiness
- Collaborate closely with DevOps and ML Engineering teams to define and implement best practices for MLOps, ensuring seamless integration of models into the production environment.
- Architect and oversee the deployment of scalable, high-availability, and low-latency inference services.
- Ensure all AI development adheres to strict governance, compliance, and ethical AI standards.
Required Qualifications
- Experience: 6+ years of progressive experience in Data Science, Machine Learning, or Applied AI research, with a minimum of 3 years in a leadership or principal role.
- Technical Depth: Expert proficiency in Python and ML frameworks (PyTorch, TensorFlow). Deep knowledge of statistical modelling, machine learning fundamentals, and distributed computing.
- Ownership Track Record: Demonstrated history of successfully taking ownership of complex, ambiguous AI projects and driving them from initial concept/POC to stable production release.
- Architecture: Strong understanding of Microservices architecture and experience designing cloud-native ML solutions (AWS, GCP, or Azure).
- Communication: Exceptional ability to distill complex technical and mathematical concepts into clear business implications for executive leadership.
- Education: Master’s or Ph.D. in Computer Science, Applied Mathematics, Engineering, or a related quantitative field.