Job Description

Technical Lead – Data Science & Generative AI

n

Incedo Platform & Solutions| Applied AI, Python & LLMs

n

LOCATION: Gurugram

n

EXPERIENCE: 6–10 years

n

REPORTS TO: Engineering Manager

n

n

ABOUT INCEDO

n

Incedo is a global AI and data transformation specialist, helping companies turn digital investment into sustainable business impact by delivering ROI from AI@Scale . We are 4,000+ people across the US, Canada, Latin America and India, working with Fortune 500 enterprises and quick-growing clients in banking & payments, wealth management, telecom, hi-tech and life sciences.

n

Build what's next in AI, data and enterprise platforms

n

Our Platform & Solutions portfolio is where Incedo builds AI-native products for real enterprise problems: Incedo Lighthouse (AI-powered decision intelligence), DataXel (agentic data modernization), brAInspark (agentic AI enablement), IncedoPay (integrated payables), Kratos (regulatory compliance and data control for banking) and DQXpert (AI-powered data quality) — plus domain products across customer support, document processing, quality engineering, healthcare and financial services.

n

WHY THIS ROLE

n

This is a builder’s role with a leader’s remit. You will be hands-on in Python and model code most weeks — and the person the team’s work is measured through.

n

You decide what “production-ready” means here, and you explain the result to a stakeholder who does not care about your architecture — only whether they can act on it.

n

THE ROLE

n

As Technical Lead – Data Science & Generative AI , you drive the design,



development and deployment of machine learning, deep learning and generative AI solutions across Incedo’s platforms and client programmes.

n

You work alongside data scientists, data engineers and business stakeholders to ship scalable, production-ready AI/ML applications , shape the technical roadmap for AI initiatives, and mentor the team that builds it.

n

WHAT YOU'LL OWN

n

1. Models in production

n

n
- Build, train, tune and deploy ML and deep learning models using TensorFlow or PyTorch and scikit-learn.
n
- Own model evaluation, optimisation, monitoring and governance — robustness, fairness, drift and scale, not just offline accuracy.
n
- Do the unglamorous work well: preprocessing, wrangling and feature engineering with pandas and NumPy.
n

n

2. GenAI and LLM applications

n

n
- Develop GenAI applications — conversational agents, summarisation, RAG pipelines, document intelligence.
n
- Design retrieval and prompting strategies that hold up on messy enterprise data — and recognise when fine-tuning is the better answer.
n
- Instrument outputs so quality is measurable rather than anecdotal.
n

n

3. Pipelines and engineering rigour

n

n




- Partner with data engineering to design scalable data and model pipelines for training and inference.
n
- Enforce reproducibility, testing and code quality across the team’s work.
n

n

4. Leadership and communication

n

n
- Mentor data scientists, run technical reviews and promote coding excellence.
n
- Present findings and AI strategy to non-technical stakeholders in language that drives a decision.
n

n

WHAT YOU'LL BRING

n

n
- 6–10 years in data science and applied AI, including experience leading a small technical team.
n
- Expert Python: advanced OOP, data structures, API design and testing.
n
- Strong ML/DL delivery with TensorFlow and scikit-learn, and fluency in pandas and NumPy.
n
- Practical LLM and GenAI experience: Hugging Face Transformers, LangChain or LlamaIndex.
n
- Solid statistical foundations: regression, time-series, hypothesis testing and anomaly detection.
n
- Communication that bridges technical and business teams — clear, specific and actionable.
n
- A proven mentoring record and a bias for shipping over perfecting.
n

n

GOOD TO HAVE

n

n
- PyTorch for advanced deep learning and LLM fine-tuning.
n
- MLOps in practice: MLflow, Docker, Kubernetes and CI/CD.
n
- Vector databases — FAISS, Pinecone, Weaviate or Milvus.
n
- Cloud ecosystems (AWS, Azure, GCP) and big data frameworks (Spark, Hadoop).
n
- Explainability and fairness tooling such as SHAP and LIME.
n

n

EDUCATION

n

B.Tech / M.Tech / M.S. in Computer Science, Statistics, Mathematics or a closely related technical discipline. Equivalent industry experience considered.

📌 Technical Lead - Data Science & Generative AI (Gurugram)
🏢 Incedo
📍 Gurugram