Data Scientist
Reads like a ML Engineer role: putting models into production.
Gurugram, Haryana, India
- Pay
- Pay not listed
- Work setting
Hybrid — work setting source
Work setup in the employer’s posting: Hybrid
From the employer’s posting- Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting
What you’ll work on
Full postingDesign and implement agentic AI workflows using LangGraph, AutoGen, or LangChain
Architect and optimize scalable REST/WebSocket APIs for production
Develop, fine-tune, and integrate LLMs into enterprise applications
Deploy and maintain GenAI and agentic AI projects in production
Implement Neo4j graph databases for knowledge graph use cases
From the employer’s posting
Lead the design and implementation of agentic AI workflows using LangGraph, AutoGen, LangChain, or similar frameworks.
Architect and optimize scalable APIs (REST/WebSocket) for production deployment.
Develop, fine-tune, and integrate large language models into enterprise applications.
Deploy and maintain at least 3 GenAI/Agentic AI projects in production, ensuring reliability, scalability, and performance.
Implement graph databases (Neo4j) for knowledge graph-based use cases.
Education & alternatives
- Contributions to open-source AI/ML projects or published research in generative AI. - Bachelor’s or Master’s degree in Computer Science, Data Science, Data Engineering, AI/ML, or related field. - 3+ years of total professional experience in AI/ML, Data Science, or related fields.
- Practical knowledge of SQL, No-SQL, vector databases, and graph databases. Bachelor's/Master's in Engineering 2-5 years
Tools in this posting
- Python
- SQL
- MongoDB
- Neo4j
- PyTorch
- TensorFlow
- PostgreSQL
Source — Tool mentions in context
- 3+ years of total professional experience in AI/ML, Data Science, or related fields. - 3+ years of hands-on experience in Python with strong software engineering practices. - At least 3 years of experience in AI/ML engineering, including 2+ years of hands-on experience in Generative AI /Agentic AI.
- Deploy and maintain at least 3 GenAI/Agentic AI projects in production, ensuring reliability, scalability, and performance. - Integrate SQL, No-SQL, and vector databases such as Postgres, MongoDB, and ChromaDB. - Implement graph databases (Neo4j) for knowledge graph-based use cases.
- Strong background in Data Science or Data Engineering, including data pipelines, ETL, and data modeling. - Practical knowledge of SQL, No-SQL, vector databases, and graph databases. Bachelor's/Master's in Engineering 2-5 years
- Integrate SQL, No-SQL, and vector databases such as Postgres, MongoDB, and ChromaDB. - Implement graph databases (Neo4j) for knowledge graph-based use cases. - Provide technical leadership and mentorship to engineering teams.
- At least 3 years of experience in AI/ML engineering, including 2+ years of hands-on experience in Generative AI /Agentic AI. - Hands-on experience with deep learning frameworks (PyTorch, TensorFlow). - Expertise in large language models (LLMs), prompt engineering, and fine-tuning.
Posting record
Report an errorLikely evergreenReposted 2 times since Sep 18, 2026. A role that keeps coming back is often kept open to collect applications rather than to fill one seat.
- Status in our records
- Live
- First seen by us
- Oct 20, 2026 · 10 days ago
- Recorded sightings
- 70
- Last seen by us
- Oct 29, 2026
- Reposted
- 2 times since Sep 18, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications. We count a repost when the same title and location comes back as a new listing within 60 days of the last one coming down. Some hiring systems expire listings on a schedule, so repost rates vary by system.
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- Have your resume and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the pay range before committing time to the process.
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Job description
- Lead the design and implementation of agentic AI workflows using LangGraph, AutoGen, LangChain, or similar frameworks.
- Architect and optimize scalable APIs (REST/WebSocket) for production deployment.
- Develop, fine-tune, and integrate large language models into enterprise applications.
- Deploy and maintain at least 3 GenAI/Agentic AI projects in production, ensuring reliability, scalability, and performance.
- Integrate SQL, No-SQL, and vector databases such as Postgres, MongoDB, and ChromaDB.
- Implement graph databases (Neo4j) for knowledge graph-based use cases.
- Provide technical leadership and mentorship to engineering teams.
- Collaborate with cross-functional stakeholders to identify and deliver GenAI solutions across multiple business domains.
- Ensure robustness, security, and compliance of deployed AI systems.
- Stay current with trends in GenAI, deep learning, and orchestration frameworks.
- Document and present technical solutions to both technical and non-technical audiences.
- Familiarity with LangGraph, AutoGen, LangChain, or similar agentic AI frameworks.
- Experience with multi-agent systems and orchestration of intelligent workflows.
- Knowledge of MLOps practices (CI/CD, model monitoring, retraining pipelines).
- Strong problem-solving skills and ability to thrive in fast-paced R&D environments.
- Contributions to open-source AI/ML projects or published research in generative AI.
- Bachelor’s or Master’s degree in Computer Science, Data Science, Data Engineering, AI/ML, or related field.
- 3+ years of total professional experience in AI/ML, Data Science, or related fields.
- 3+ years of hands-on experience in Python with strong software engineering practices.
- At least 3 years of experience in AI/ML engineering, including 2+ years of hands-on experience in Generative AI /Agentic AI.
- Hands-on experience with deep learning frameworks (PyTorch, TensorFlow).
- Expertise in large language models (LLMs), prompt engineering, and fine-tuning.
- Proven track record of deploying at least 3 GenAI/Agentic AI projects into production.
- Strong background in Data Science or Data Engineering, including data pipelines, ETL, and data modeling.
- Practical knowledge of SQL, No-SQL, vector databases, and graph databases.
Bachelor's/Master's in Engineering 2-5 years
Source details
Source notes
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- Pay
No pay amount identified in the saved description.
- Location & working pattern
Gurugram, Haryana, India
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
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