Senior Data Scientist

We are hiring a Senior Data Scientist to lead the design and deployment of production-grade AI and machine learning solutions. You will own the full lifecycle from problem framing to model deployment working across NLP, generative AI, recommendation systems, and document intelligence. This is a hands-on role with direct impact on enterprise AI strategy, particularly in complex, data-rich industries such as Oil & Gas, Energy, and Manufacturing.

Requirements

CORE TECHNOLOGY STACK

• Hugging Face
• LangChain / LlamaIndex
• RAG Pipelines
• Vector Databases
• spaCy / NLTK PyTorch / TensorFlow
• Azure / AWS Docker & APIs
• Python
• SQL Prompt Engineering
• LLM Fine-Tuning

KEY RESPONSIBILITIES

Machine Learning & Modelling

▸ Design, build, and deploy ML models to solve complex, ambiguous business challenges across structured and unstructured data

▸ Build recommendation engines and decision-support systems with measurable impact on business outcomes

▸ Develop predictive and prescriptive analytics solutions that move beyond reporting into actionable intelligence

NLP, Generative AI & Document Intelligence

▸ Develop and optimize information extraction pipelines for technical reports, manuals, contracts, and domain-specific corpora

▸ Build document intelligence solutions that convert unstructured enterprise content into structured, queryable knowledge

▸ Fine-tune and evaluate large language models (LLMs) for enterprise use cases including summarization, classification, and Q&A

▸ Develop RAG solutions and knowledge-based AI assistants grounded in enterprise data with production-level reliability

Production & Platform

▸ Deploy AI solutions on cloud-native architectures with focus on scalability, observability, and maintainability

▸ Partner with data engineering teams to build robust data and AI platforms that support model training and serving at scale

▸ Own model performance monitoring post-deployment drift detection, feedback loops, and retraining triggers

REQUIRED QUALIFICATIONS & SKILLS

• Experience: 6+ years in data science, ML, or AI engineering in production settings
• Python: Strong programming skills for data wrangling, modelling, and API development
• NLP Frameworks: spaCy, Transformers, Hugging Face, NLTK , hands-on, not just familiar
• GenAI Stack: LLMs, RAG architectures, vector databases (Pinecone, Weaviate, pgvector)
• Recommendation: Experience building ranking models and collaborative/content-based systems
• Data Skills: Strong SQL, data manipulation, and feature engineering at scale
• Cloud Platforms: Azure and/or AWS , model training, serving, and pipeline orchestration
• Deployment: Docker, REST APIs, CI/CD pipelines, and production ML deployment patterns

PREFERRED

Domain experience in Oil & Gas, Energy, Manufacturing, or large industrial enterprises is a significant advantage.

Candidates with this background will move to the front of the pipeline. Familiarity with technical document types — well reports, P&IDs, maintenance logs is particularly valued.

Also valuable:

• MLflow / experiment tracking
• Prompt optimization & evaluation frameworks
• Knowledge graph experience
• Published research or open-source contributions