AI/ML Subject Matter Expert – Data Analytics & GenAI

We are looking

for a highly experienced AI/ML Subject Matter Expert with strong Data Analytics

and GenAI implementation expertise. The role requires a hands-on individual

contributor who can design, build, and operationalize scalable analytics

solutions, KPI computation frameworks, data validation pipelines, machine

learning models, and governed GenAI capabilities over enterprise data.

The ideal

candidate will bring deep expertise in Python, SQL, analytics engineering,

machine learning, Retrieval-Augmented Generation, and Agentic AI frameworks,

and should be comfortable working with complex business metrics, enterprise

data sources, and open-source LLM technologies to deliver reliable,

explainable, and production-ready analytical solutions.

Responsibilities:

Analytics Engineering & KPI Development:

• Design, develop, and maintain robust analytics

code using Python, pandas, and NumPy to compute, validate, reconcile, and

operationalize key business metrics including costing, margins, QBR metrics,

and operational performance indicators.

• Build efficient data transformations, optimize

performance through vectorization and memory management, and implement

repeatable data pipelines with testing, logging, validation, and reconciliation

controls.

SQL & Data Extraction:

• Develop advanced SQL queries to extract,

transform, and shape data from enterprise systems and cloud data warehouse

platforms.

• Work with complex joins, aggregations, window

functions, query optimization, and governed metric definitions.

Generative AI / Ask-the-Data Prototype:

• Implement a governed GenAI prototype enabling

users to ask questions over structured and semi-structured enterprise data.

• Use Llama-family or comparable open-source models

through Ollama, llama.cpp, vLLM, or similar inference frameworks.

• Build Retrieval-Augmented Generation and Agentic

AI pipelines across structured and semi-structured data.

• Design chunking, embedding, retrieval, and

reranking approaches.

• Implement agentic workflows using CrewAI,

LangGraph, AutoGen, LlamaIndex Agents, or equivalent production-ready agent

orchestration frameworks.

• Produce structured responses such as tables,

JSON, and drill-down-ready answers.

• Implement guardrails for grounded responses,

citations, traceability to source data, and safe handling of sensitive fields.

• Support evaluation of GenAI and agentic outputs

for accuracy, groundedness, reliability, latency, and operational usability.

Machine Learning & Advanced Analytics:

• Apply light-to-moderate machine learning

techniques where appropriate, including anomaly detection, outlier

identification, cost variance analysis, feed failure detection, simple

forecasting, trend analysis, model evaluation, and error analysis.

Experimentation, Evaluation & Deployment:

• Create reproducible experimentation workflows,

including test question sets for LLM evaluation, accuracy and groundedness

checks, latency profiling, and performance tuning.

• Package deliverables for deployment using Docker,

configuration management, and produce clear technical documentation, runbooks,

and handover materials.

Requirements

Education:

Relevant

degree in Computer Science, Data Science, Engineering, Mathematics, Statistics,

or a related field.

Technical Skills (Must haves):

• Expert-level Python skills, particularly with

pandas and NumPy: data cleaning and transformation; joins, merges,

aggregations, and windowed calculations; time-series data handling; performance

optimization, profiling, and memory management.

• Strong SQL expertise, including complex joins,

aggregates, window functions, and query tuning and optimization mindset.

• Solid understanding of statistics and machine

learning fundamentals, including feature engineering, model evaluation metrics,

overfitting and validation concepts, and scikit-learn or equivalent ML

libraries.

• Practical GenAI implementation experience with

Llama models or comparable open-source LLMs; Ollama or similar local inference

tools; RAG and Agentic AI frameworks such as LangChain, LlamaIndex, LangGraph,

CrewAI, AutoGen, or equivalent; and embeddings/vector stores such as FAISS,

pgvector, Weaviate, or Pinecone.

• Strong engineering discipline, including unit

testing and data testing, logging and error handling, Git-based development

workflows, CI basics, Docker, and environment management.

Need to have (Can be bridged):

• Hands-on individual contributor experience

driving solution development from prototype through deployment-ready

deliverables.

• Ability to work with complex business metrics,

enterprise data sources, and governed analytical solutions.

• Ability to collaborate with business and

technology stakeholders and independently drive solution development.

Good to have (Not essential):

• Experience with Snowflake or comparable modern

cloud data platforms.

• dbt experience, including modeling, testing, and

documentation.

• Experience with enterprise semantic layers or

governed metric definitions.

• Experience building lightweight APIs using

FastAPI or similar frameworks.

• Familiarity with enterprise security concepts

such as RBAC, data masking, sensitive data handling, and audit logging.

Preferred Qualifications (Optional):

Experience

across the typical technology stack: Python, pandas, NumPy, SQL, scikit-learn,

Jupyter, Git, Docker, FastAPI, LangChain, LlamaIndex, Ollama, Llama-family

models, FAISS, pgvector, Weaviate, Pine cone, and Snowflake or equivalent cloud

data warehouse.