Senior AI Developer (Industrial AI & GenAI Platforms)

About the Role:

Senior / Principal AI Developer

machine learning, time-series analytics, and model development

reliable, explainable, and scalable AI

Key Responsibilities:

AI / ML Model Development

• Design, develop, and deploy

  machine learning models

  for:
• Time-series forecasting
• Anomaly detection
• Trend and pattern analysis
• Own the

  entire model lifecycle

  :
• Data preparation & feature engineering
• Model selection and training
• Evaluation and validation
• Deployment, monitoring, and retraining
• Ensure models are

  robust, explainable, and production-ready

  .

Time-Series & Data Analytics

• Work with

  time-series and event-based data

  from large-scale systems.
• Handle missing data, noisy signals, drift, and delayed labels.
• Define metrics that balance

  accuracy, cost, and operational risk

  .

GenAI & NLP Capabilities

• Build

  LLM-powered features

  for analytics, insights, and intelligent interaction.
• Implement

  Retrieval-Augmented Generation (RAG)

  using structured and unstructured data.
• Apply

  NLP techniques

  for text normalization, classification, and summarization.
• Design

  agent-based workflows (MCP-style)

  with clear guardrails, tool contracts, and auditability.

Cloud-Native & API Development

• Build

  cloud-native AI services

  on Microsoft Azure.
• Design and develop

  RESTful APIs

  for AI model inference and analytics.
• Implement scalable microservices using Docker and Kubernetes.
• Ensure security, performance, and reliability of AI services.
  

MLOps & Production Readiness

• Implement CI/CD pipelines for models and AI services.
• Handle model versioning, monitoring, and drift detection.
• Build observability for AI systems (logs, metrics, tracing, cost tracking).
• Apply Responsible AI principles and data governance.
  

Required Skills:

Core (Mandatory)

• Strong proficiency in

  Python

• Hands-on experience in

  ML model development

• Strong experience with

  time-series analysis and forecasting

• Experience with

  PyTorch / TensorFlow / scikit-learn

• Solid understanding of

  statistics and ML fundamentals

Platform & Cloud

• Experience building

  cloud-native applications

  (Azure preferred)
• RESTful API and microservices development
• Docker and Kubernetes
• MLOps practices
  

GenAI & NLP

• Experience with

  LLMs

  and prompt engineering
• Hands-on with

  RAG architectures

• NLP fundamentals
• Familiarity with

  MCP / agent-based systems

  (or equivalent)

Good to Have

• Experience in

  industrial, manufacturing, energy, utilities, or enterprise platforms

• Digital Twin or Asset Analytics exposure
• Responsible AI, explainability (SHAP, LIME)
• Product-based R&D experience

What We Are Looking For

• Engineers who think beyond notebooks and demos
• Strong ownership mindset

  design it, build it, run it

• Ability to explain AI decisions to non-AI stakeholders
• Focus on

  reliability, scalability, and real business impact

Why Join Us

• Work on

  enterprise-scale AI systems

  used in real operations
• Exposure to complex, high-impact AI problems
• Collaborative global R&D environment
• Opportunity to influence AI architecture and long-term strategy