Job Description :

We are seeking a highly experienced and innovative Lead Artificial Intelligence Engineer to drive the design, development, and deployment of enterprise-grade AI solutions.

The ideal candidate will possess deep expertise in Machine Learning, Generative AI, Large Language Models (LLMs), MLOps, and cloud-based AI architectures.

This role involves leading AI initiatives, mentoring engineering teams, collaborating with business stakeholders, and delivering scalable AI products that create measurable business impact.

As an AI Engineering Lead, you will play a critical role in defining AI strategy, building intelligent systems, and establishing best practices for AI development across the organization.

Key Responsibilities :

AI Solution Architecture & Development :

- Design, develop, and deploy scalable AI/ML solutions for business-critical applications.

- Lead the architecture and implementation of end-to-end AI systems, from data ingestion to model deployment and monitoring.

- Build advanced Machine Learning and Deep Learning models for prediction, classification, recommendation, optimization, and automation use cases.

- Develop and integrate Generative AI applications powered by Large Language Models (LLMs).

- Design Retrieval-Augmented Generation (RAG) frameworks for enterprise knowledge management and intelligent search systems.

- Optimize AI models for performance, scalability, reliability, and cost efficiency.

Generative AI & LLM Engineering :

- Develop AI applications using OpenAI, Anthropic, Gemini, Llama, Mistral, and other foundation models.

- Fine-tune and customize LLMs for domain-specific business use cases.

- Implement prompt engineering, agentic AI workflows, function calling, and multi-agent architectures.

- Build conversational AI platforms, intelligent assistants, chatbots, and AI-powered automation solutions.

- Evaluate model performance using benchmarking, testing frameworks, and AI observability tools.

MLOps & Production Engineering :

- Establish MLOps pipelines for model training, deployment, monitoring, and lifecycle management.

- Implement CI/CD practices for AI and Machine Learning workflows.

- Develop automated model retraining and performance monitoring frameworks.

- Ensure model governance, explainability, version control, and compliance standards.

- Monitor production AI systems and continuously improve model effectiveness.

Data Engineering & AI Infrastructure :

- Collaborate with data engineering teams to build scalable data pipelines.

- Design feature engineering frameworks and data preparation workflows.

- Work with structured, semi-structured, and unstructured datasets at scale.

- Optimize AI workloads using distributed computing frameworks and GPU infrastructure.

- Manage vector databases and embedding pipelines for GenAI applications.

Leadership & Stakeholder Management :

- Lead and mentor a team of AI Engineers, Data Scientists, and ML Engineers.

- Define technical roadmaps and AI adoption strategies aligned with business goals.

- Collaborate with product managers, architects, and business leaders to identify AI opportunities.

- Conduct design reviews, code reviews, and technical assessments.

- Drive innovation by evaluating emerging AI technologies and industry trends.

Required Skills & Qualifications:

Technical Expertise:

- 1012 years of experience in Software Engineering, Artificial Intelligence, Machine Learning, or related domains.

- Strong experience in Python and AI/ML development frameworks.

- Expertise in Machine Learning, Deep Learning, NLP, Computer Vision, and Predictive Analytics.

Hands-on experience with:

i. TensorFlow

ii. PyTorch

iii. Scikit-learn

iv. Hugging Face

v. LangChain

vi. LlamaIndex

vii. OpenAI APIs

viii. Vector Databases (Pinecone, Weaviate, ChromaDB, Milvus)

- Strong understanding of Generative AI and Large Language Models.

- Experience building RAG-based applications and AI agents.

- Knowledge of prompt engineering and LLM evaluation methodologies.

Cloud & Platform Experience:

- Strong experience with at least one cloud platform:

i. AWS

ii. Azure

iii. Google Cloud Platform (GCP)

- Experience with AI services such as Azure OpenAI, AWS Bedrock, Vertex AI, and SageMaker.

- Knowledge of Docker, Kubernetes, and microservices architecture.

- Familiarity with MLOps tools including MLflow, Kubeflow, Airflow, and Databricks.

Data & Analytics:

- Strong SQL and database skills.

- Experience with data lakes, data warehouses, and big data ecosystems.

- Understanding of feature stores, model registries, and AI observability platforms.

Preferred Qualifications:

- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.

- Experience leading AI transformation initiatives within large enterprises.

- Knowledge of Responsible AI, AI Governance, and AI Security best practices.

- AI/ML, Cloud, or Data certifications are highly desirable.

- Experience working in domains such as BFSI, Healthcare, Retail, Telecom, Manufacturing, or SaaS.

Key Competencies:

- AI Strategy & Innovation

- Technical Leadership

- Problem Solving & Analytical Thinking

- Solution Architecture

- Team Mentorship & Development