Lead-AI Engineering

JOB DESCRIPTION

Adani Ports and Special Economic Zone Limited (APSEZ) is the largest commercial ports operator in India accounting for nearly one-fourth of the cargo movement in the country. Its presence across 15 domestic ports in seven maritime states of Gujarat, Maharashtra, Goa, Kerala, Andhra Pradesh, Tamil Nadu and Odisha presents the most widespread national footprint with deepened hinterland connectivity. The port facilities are equipped with the latest cargo-handling infrastructure which is not only best-in-class, but also capable of handling the largest vessels calling at Indian shores. Our ports are equipped to handle diverse cargos, from dry cargo, liquid cargo, crude to containers.

Role Purpose

The AI Engineering Lead will be responsible for end-to-end delivery, architecture, and scaling of AI-driven platforms and solutions across the organization. This role bridges business problems with production-grade AI systems, owning AI solution architecture, model engineering, ML Ops, data pipelines, and AI product delivery, while leading and mentoring a high-performing AI engineering team. The role emphasizes practical, scalable, and secure AI implementation rather than pure research.

Responsibilities

AI Architecture & Technical Leadership

• Define and own the enterprise AI architecture covering:
• Predictive, prescriptive, and generative AI use cases
• ML, Deep Learning, and GenAI workloads
• Design and govern end-to-end AI pipelines :
• Data ingestion → feature engineering → model training → deployment → monitoring
• Architect LLM-based solutions using:
• Prompt engineering, RAG (Retrieval Augmented Generation), agent-based frameworks
• Integration with enterprise data platforms (data lakes, warehouses, APIs)
• Establish AI design standards, code quality, security, and reusability frameworks .
• Ensure AI solutions meet scalability, performance, reliability, and compliance expectations.

AI Solution Development & Engineering

Lead hands-on development of:

• Machine Learning & Deep Learning models
• Generative AI applications (LLMs, chatbots, copilots, automation agents)

Drive adoption of AI engineering best practices , including:

• Feature stores
• Model versioning
• Experiment tracking
• Evaluation and explainability
• Integrate AI solutions with enterprise applications, APIs, and digital platforms.

Drive API-first, modular, and cloud-native AI engineering.

MLOps, LLMOps & Platform Enablement

MLOps / LLMOps strategy:

• CI/CD for models
• Model monitoring, drift detection, retraining pipelines

Define standards for:

• Model deployment (batch, real-time, streaming)
• Observability, performance, and cost optimization

Partner with Cloud & DevOps teams to operationalize AI at scale on:

• Cloud platforms (Azure / AWS / GCP)

Databricks / Lakehouse / ML platforms

Delivery Ownership & Program Execution

Own end-to-end AI program delivery, from use-case prioritization to production rollout.

Translate business problems into clear AI problem statements, roadmaps, and execution plans.

Manage Agile delivery for AI initiatives:

• Sprint planning, estimation, backlog prioritization, releases

Proactively manage AI risks :

• Data quality
• Bias
• Security
• Regulatory compliance

Ensure predictable delivery aligned with business KPIs and value realization.

Team Leadership & Capability Building

Build and lead a high-performing team of:

• AI Engineers
• ML Engineers
• Data Engineers

Mentor teams on:

• Production-grade AI design
• Responsible AI practices
• Engineering discipline over experimentation-only approaches

Conduct performance reviews, capability planning, hiring, and succession planning.

Establish a strong AI engineering culture focused on ownership, innovation, and measurable impact.

Stakeholder & Business Collaboration

Partner with:

• Business leaders
• Product teams
• Data, Platform, Security, and IT teams

Act as AI advisor to leadership for:

• Use-case identification
• Feasibility assessments
• ROI-driven AI prioritization

Support executive discussions, steering committees, and board-level updates on AI initiatives.

Education Qualification

BE / B. Tech(Computer Science)

Total Experience

10+ years of overall engineering experience with 5+ years in AI / ML / Data Engineering

Cloud and AI Certification will be added advantage