Senior AI & Generative AI Specialist

Role Summary

We are seeking a Senior AI & Generative AI Specialist to architect, build, and scale production-grade AI and GenAI solutions. The role demands deep hands-on expertise, strong system architecture skills, and the ability to lead cross-functional teams delivering result oriented & compliant AI systems.

This role will own end-to-end AI lifecycle — from problem framing and model design to deployment, monitoring, governance, and business impact — with a strong emphasis on Machine learning, GenAI, LLM fine-tuning, RAG systems, and Responsible AI.

Key Responsibilities

AI & GenAI Architecture

• Design and architect enterprise-scale AI and Generative AI systems, including LLM-based applications, RAG pipelines, fine-tuned models, and multimodal AI systems.
• Lead development of AI platforms and frameworks enabling reusable, scalable AI services (AI-as-a-Service).
• Define model selection strategies , fine-tuning approaches, and inference optimization.

Machine Learning & Deep Learning

• Develop and deploy advanced ML/DL models across:
  • Computer Vision (segmentation, detection, classification)
  • NLP (BERT, GPT, Transformers)
  • Generative AI (Diffusion models, GANs, multimodal systems)
  • Time-series forecasting, predictive analytics, anomaly detection
• Drive model optimization, hyperparameter tuning, and performance benchmarking.
• Ensure model explainability, fairness, bias detection, and mitigation.

GenAI & LLM Systems

• Build GenAI applications & Agents including:
  • Intelligent document processing
  • Automated report generation
  • Smart ticketing and customer escalation systems
  • Knowledge assistants using RAG + vector databases
• Implement prompt engineering, evaluation frameworks, and guardrails.
• Optimize inference cost, latency, and scalability in cloud environments.

MLOps & Production Deployment

• Establish MLOps best practices:
  • CI/CD for ML
  • Model versioning and monitoring
  • Automated retraining pipelines
• Deploy AI services using Docker, Kubernetes, MLflow, FastAPI, Flask.
• Ensure high availability, low latency, and cloud cost optimization.

Cloud & Big Data

• Architect AI workloads on Azure, Databricks, Spark.
• Build scalable data pipelines for large-scale training and inference.
• Leverage distributed computing for large datasets and real-time inference.

Leadership & Stakeholder Engagement

• Consult and mentor AI engineering and data science teams.
• Collaborate with the AI working group & international stakeholder community.
• Translate business and domain problems into AI solutions with measurable impact.
• Drive innovation initiatives, patents, and hackathon-level experimentation.

 

• BE/Btech/Master’s degree in Data Science, AI, or related field
• Experience in AI , Agentic AI , Advance data analytics usecases in a manufacturing environment
• Strong understanding of AI governance and compliance
• >8 years of experience in buildup and delivery of AI/ML usecases with proven business benefits
• Leadership of AI/ML teams would be an added advantage

 

Required Technical Skills

Programming & Frameworks

• Python (expert), PyTorch, Keras, PySpark, SQL
• REST API development: FastAPI, Flask
• Version control & CI/CD: Git, GitHub Actions

AI / ML

• Supervised & Unsupervised Learning
• Deep Learning: CNNs, RNNs, Transformers
• Generative AI: LLMs, Diffusion models, GANs
• Reinforcement Learning (applied understanding)

NLP & Computer Vision

• BERT, GPT, Text Summarization, NER
• Speech-to-Text / Text-to-Speech
• Image segmentation, object detection, multimodal AI

Cloud & MLOps

• AWS, Azure, Databricks, Spark
• Docker, Kubernetes, MLflow
• Scalable inference engines

Impact & Success Metrics

• Deliver AI systems with measurable business outcomes (efficiency, accuracy, cost reduction).
• Reduce manual workloads through automation and GenAI adoption.
• Improve decision-making accuracy using explainable and responsible AI.
• Scale AI platforms adopted across multiple plants & divisions