Job Description
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