AI Engineer

1. Role Purpose

• Lead the hands-on design, development, testing and implementation of AI, Machine Learning and GenAI use cases.
• Translate business opportunities into practical, measurable and production-ready AI solutions.
• Work closely with business, architecture, data, security and application teams to take AI use cases from idea → PoC → pilot → production.

2. AI Use Case Design & Prioritization

• Identify and assess AI opportunities based on business value, feasibility, data availability, complexity and risk.
• Translate business problems into clearly defined AI/ML/GenAI use cases and technical requirements.
• Define measurable success criteria and support prioritization of quick wins and strategic use cases.

3. Hands-on AI/ML & GenAI Engineering

• Design, develop and test solutions for anomaly detection, classification, risk scoring, prediction, pattern detection and intelligent automation.
• Develop enterprise GenAI capabilities using LLMs, Azure OpenAI, RAG, embeddings, semantic/vector search and document intelligence.
• Design controlled AI assistants and AI agents integrated with enterprise APIs, data and workflows.
• Use Python, SQL and relevant AI/ML frameworks for hands-on development, prototyping and troubleshooting.

4. Data & AI Integration

• Work with data teams to profile, cleanse, transform and prepare AI-ready datasets.
• Validate data completeness, quality and suitability for AI models.
• Integrate AI models and services with enterprise applications, APIs and data platforms.
• Support structured, semi-structured and unstructured data use cases.

5. Responsible AI, Security & Quality

• Implement Responsible AI controls covering accuracy, explainability, privacy, security, bias and human oversight.
• Establish human-in-the-loop controls for sensitive or high-impact AI decisions.
• Test GenAI solutions for correctness, groundedness, consistency, hallucination, prompt injection and data leakage.
• Define AI evaluation criteria and quality thresholds before production deployment.

6. MLOps / LLMOps & Production

• Establish model, prompt and configuration versioning, testing and deployment processes.
• Monitor model performance, drift, failures and usage.
• Support secure production deployment and continuous improvement of AI capabilities.
• Ensure AI models, prompts, data and outputs remain traceable and auditable.

7. Required Experience & Skills

• 7+ years of relevant data, analytics or software-engineering experience with strong recent AI/ML/GenAI delivery experience.
• Strong hands-on Python, SQL, Azure AI and Azure OpenAI capability.
• Practical experience with LLMs, RAG, AI agents, machine learning, vector search, document intelligence and enterprise integration.
• Experience taking AI solutions from use-case definition through PoC/pilot to production.
• Knowledge of MLOps/LLMOps, AI security and Responsible AI.
• Microsoft Azure AI Engineer certification preferred; relevant Data Engineering/Data Science certifications advantageous.

Pay: ₹480,000.00 - ₹1,000,000.00 per year

Benefits:

• Flexible schedule
• Work from home

Work Location: Remote