Job Information

- Department Name Applied AI & Data Engineering
- Job Type Full time
- Date Opened 30/08/2026
- Industry Software Development
- Minimum Experience In Years 4
- Maximum Experience In Years 10
- City Chennai
- Province Tamil Nadu
- Country India
- Postal Code 600089

About Us

MulticoreWare is a global software solutions & products company with its HQ in San Jose, CA, USA. With worldwide offices, it serves its clients and partners in North America, EMEA and APAC regions. Started by a group of researchers, MulticoreWare has grown to serve its clients and partners on HPC & Cloud computing, GPUs, Multicore & Multithread CPUS, DSPs, FPGAs and a variety of AI hardware accelerators.

MulticoreWare was founded by a team of researchers that wanted a better way to program for heterogeneous architectures. With the advent of GPUs and the increasing prevalence of multi-core, multi-architecture platforms, our clients were struggling with the difficulties of using these platforms efficiently.

We started as a boot-strapped services company and have since expanded our portfolio to span products and services related to compilers, machine learning, video codecs, image processing and augmented/virtual reality. Our hardware expertise has also expanded with our team; we now employ experts on HPC and Cloud Computing, GPUs, DSPs, FPGAs, and mobile and embedded platforms. We specialize in accelerating software and algorithms, so if your code targets a multi-core, heterogeneous platform, we can help.

Job Description

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:

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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.
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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:

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Develop advanced SQL queries to extract, transform, and shape data from enterprise systems and cloud data warehouse platforms.
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Work with complex joins, aggregations, window functions, query optimization, and governed metric definitions.

Generative AI / Ask-the-Data Prototype:

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Implement a governed GenAI prototype enabling users to ask questions over structured and semi-structured enterprise data.
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Use Llama-family or comparable open-source models through Ollama, llama.cpp, vLLM, or similar inference frameworks.
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Build Retrieval-Augmented Generation and Agentic AI pipelines across structured and semi-structured data.
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Design chunking, embedding, retrieval, and reranking approaches.
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Implement agentic workflows using CrewAI, LangGraph, AutoGen, LlamaIndex Agents, or equivalent production-ready agent orchestration frameworks.
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Produce structured responses such as tables, JSON, and drill-down-ready answers.
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Implement guardrails for grounded responses, citations, traceability to source data, and safe handling of sensitive fields.
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Support evaluation of GenAI and agentic outputs for accuracy, groundedness, reliability, latency, and operational usability.

Machine Learning & Advanced Analytics:

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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:

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Create reproducible experimentation workflows, including test question sets for LLM evaluation, accuracy and groundedness checks, latency profiling, and performance tuning.
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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):

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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.
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Strong SQL expertise, including complex joins, aggregates, window functions, and query tuning and optimization mindset.
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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.
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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.
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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):

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Hands-on individual contributor experience driving solution development from prototype through deployment-ready deliverables.
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Ability to work with complex business metrics, enterprise data sources, and governed analytical solutions.
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Ability to collaborate with business and technology stakeholders and independently drive solution development.

Valuable to have (Not essential):

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Experience with Snowflake or comparable modern cloud data platforms.
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dbt experience, including modeling, testing, and documentation.
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Experience with enterprise semantic layers or governed metric definitions.
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Experience building lightweight APIs using FastAPI or similar frameworks.
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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.

📌 AI/ML Subject Matter Expert – Data Analytics & GenAI (Chennai)
🏢 MulticoreWare
📍 Chennai