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Job Description
Structured overview of role & requirementsAbout This Role
Design, develop, and deploy enterprise-scale AI, Generative AI, and Agentic AI solutions including Retrieval Augmented Generation (RAG) applications and AI APIs/microservices.
Lead AI/ML project execution independently, including cloud-native MLOps practices (CI/CD, Kubernetes, Docker), model lifecycle management, and performance optimization.
Mentor junior engineers, set engineering standards, and collaborate with business stakeholders to translate problems into scalable AI solutions.
Minimum Requirements
Strong programming skills in Python, SQL, REST APIs, and Git/GitHub.
Experience with AI technologies including machine learning, deep learning, NLP, large language models, Generative AI, prompt engineering, and Agentic AI frameworks.
Proficiency in cloud platforms (Azure preferred, AWS, GCP), MLOps/LLMOps tools (MLflow, Azure ML, Databricks), and container/orchestration technologies (Docker, Kubernetes).
Work Experience Required: Not explicitly mentioned in the JD; Education: Bachelor’s or Master’s degree in Computer Science, Data Science, AI, Machine Learning, Engineering, or related field.
Ideal Candidate Profile
Technically strong AI engineer with experience independently delivering production-grade AI solutions with measurable business impact.
Experienced in designing scalable, cloud-native AI architectures with expertise in Generative and Agentic AI including prompt engineering and RAG implementations.
Capable of providing technical leadership by mentoring teams and collaborating effectively with architects, product owners, and stakeholders to achieve business outcomes.