AI Engineer (GIS)

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 (Py. Torch, Tensor. Flow). 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.