Gurugram, Haryana
Open 7 months, first seen by us 214 days agoWork setup in the employer’s posting: remoteFrom the employer’s posting
Employment type Full Time
Employment type in the employer’s posting: Full TimeFrom the employer’s posting
Build, test, integrate, and deploy GenAI services and agentic workflows into production
Handle deployment, release, versioning, and rollback, and keep solutions running once live
Design and maintain Python services that connect LLMs and GenAI with client systems
Build agentic multi-step workflows using orchestration frameworks
Monitor, troubleshoot, and improve the operational health of the workflows you build
Own the hands-on delivery of AI solutions end-to-end: build, test, integrate, deploy, and ship GenAI services and agentic workflows into production.
Take solutions from prototype to production handling deployment, release, versioning, and rollback, and keep them running reliably once they are live.
Design, implement, and maintain Python-based services and workflows that integrate LLMs and GenAI capabilities with client systems and applications.
Build agentic and multi-step workflows using orchestration frameworks and platform patterns (e.g., LangGraph, AgentCore, LangChain).
Own the operational health of the workflows you build: monitoring, alerting, troubleshooting, and iterative improvement.
you turn solution designs into working, production-grade GenAI / Agentic AI workflows, and you take them all the way into production
Design, implement, and maintain Python-based services and workflows that integrate LLMs and GenAI capabilities with client systems and applications.
Qualifications: - Bachelor’s or master’s degree in computer science, Statistics, Mathematics, or a related quantitative field. - Minimum of 5 years of experience in a data science-related role, with a focus on machine learning and deep learning.
Open 7 monthsWe first saw this posting 214 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
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Day 214
Up longer than most. Similar jobs usually come down between day 8 and day 60.
Well past Thinkahead’s usual: its jobs come down by about day 43.
From 11,894 similar postings that came down, and 10 of Thinkahead’s. Coming down is not always a hire.
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GenAI Engineering & Implementation - Design, implement, and maintain Python-based services and workflows that integrate LLMs and GenAI capabilities with client systems and applications. - Build agentic and multi-step workflows using orchestration frameworks and platform patterns (e.g., LangGraph, AgentCore, LangChain).
- Minimum of 5 years of experience in a data science-related role, with a focus on machine learning and deep learning. - Strong Python coding skills with an emphasis on writing efficient, scalable, and maintainable code. - Experience with developing and training custom deep learning models using TensorFlow, PyTorch, or scikit-learn.
Nice to Have: - 5–9 years of software engineering experience, with significant hands-on development in Python for production services and workflows. - Demonstrated experience deploying and operating GenAI or agentic AI solutions in production taking them beyond prototypes and demos into live, reliable systems, including release, versioning, monitoring, and ongoing operation.
- Experience with developing and training custom deep learning models using TensorFlow, PyTorch, or scikit-learn. - Hands-on experience with Jupyter Notebooks, Azure Machine Learning Studio, Azure OpenAI, AWS SageMaker, nVidia AI Enterprise & DGX platforms - Hands-on experience with leading open source and major model providers such as LLama, Anthropic's Claude, Open AI. Etc.
- Demonstrated experience deploying and operating GenAI or agentic AI solutions in production taking them beyond prototypes and demos into live, reliable systems, including release, versioning, monitoring, and ongoing operation. - Practical, hands-on experience integrating LLM/GenAI capabilities (e.g., AWS Bedrock, Azure AI, OpenAI, Anthropic), including prompt and system design for reliability and control, and handling structured outputs (JSON schemas, tool/function calling). - Experience implementing agentic or multi-step workflows using orchestration frameworks such as LangGraph, AgentCore, or LangChain.
- Proven ability to design clear tool APIs for agents with well-defined input/output schemas, error-handling contracts, and versioning strategies. - Experience in a cloud-native AWS/Azure environment, including serverless patterns (Lambda or similar), environment configuration and secrets management, and logging, metrics, and basic observability/debugging. - Strong software engineering fundamentals: Git, testing, code review, CI/CD-friendly patterns, and clean code practices.
- Strong Python coding skills with an emphasis on writing efficient, scalable, and maintainable code. - Experience with developing and training custom deep learning models using TensorFlow, PyTorch, or scikit-learn. - Hands-on experience with Jupyter Notebooks, Azure Machine Learning Studio, Azure OpenAI, AWS SageMaker, nVidia AI Enterprise & DGX platforms
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New Data Scientist postings at the employers we follow are down 4% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.
As a Senior Technical Consultant, AI Services, you are responsible for the hands-on delivery of AI solutions within AHEAD client engagements. You are an engineer and client-advisor - you turn solution designs into working, production-grade GenAI / Agentic AI workflows, and you take them all the way into production - deployed, running, and reliable in live client environments.
You will collaborate with clients and interdisciplinary teams, which includes engineers, product owners, domain experts to understand real business needs and solve complex challenges across industries. You will work alongside senior consultants and architects, contribute to the technical direction and own delivery.
Solution Delivery & Production Deployment
Own the hands-on delivery of AI solutions end-to-end: build, test, integrate, deploy, and ship GenAI services and agentic workflows into production.
Take solutions from prototype to production handling deployment, release, versioning, and rollback, and keep them running reliably once they are live.
Make sound design and trade-off decisions as you build, and bring the hard, cross-cutting calls into the team’s technical discussions contributing to the architecture, not just consuming it.
Produce and maintain your own estimates, task breakdowns, and delivery status; surface risks, blockers, and dependencies early.
GenAI Engineering & Implementation
Design, implement, and maintain Python-based services and workflows that integrate LLMs and GenAI capabilities with client systems and applications.
Build agentic and multi-step workflows using orchestration frameworks and platform patterns (e.g., LangGraph, AgentCore, LangChain).
Develop robust tooling and APIs for agents, with clear input/output schemas, error contracts, versioning, and observability hooks.
Consume retrieval/RAG and search abstractions to improve grounding and reliability, tuning parameters (top-k, scoring, filters).
Quality, Observability & Governance
Own the operational health of the workflows you build: monitoring, alerting, troubleshooting, and iterative improvement.
Set up the observability and evaluation tooling for the solutions you build including tracing, logging, and metrics through an LLM observability stack (e.g., Langfuse, LangSmith), and quality, regression, and safety checks through evaluation frameworks (e.g., DeepEval, Ragas).
Operate within established platform, security, and governance guardrails (RBAC, data access boundaries, PII handling, logging, audit) instead of building one-off mechanisms.
Collaboration & Enablement
Partner with product managers, business stakeholders, and UX to turn problem statements and evaluation criteria into concrete, production-ready workflows.
Participate actively in design reviews, code reviews, and architecture discussions, keeping solutions maintainable, observable, and aligned to platform standards.
Support and guide junior engineers and consultants on the team through code review and pairing.
Contribute to internal enablement (playbooks, examples, reusable patterns) and act as a high adopter of AI tools (e.g., Glean, Devin, Windsurf, Claude) to accelerate design, development, testing, and documentation.
5–9 years of software engineering experience, with significant hands-on development in Python for production services and workflows.
Demonstrated experience deploying and operating GenAI or agentic AI solutions in production taking them beyond prototypes and demos into live, reliable systems, including release, versioning, monitoring, and ongoing operation.
Practical, hands-on experience integrating LLM/GenAI capabilities (e.g., AWS Bedrock, Azure AI, OpenAI, Anthropic), including prompt and system design for reliability and control, and handling structured outputs (JSON schemas, tool/function calling).
Experience implementing agentic or multi-step workflows using orchestration frameworks such as LangGraph, AgentCore, or LangChain.
Proven ability to design clear tool APIs for agents with well-defined input/output schemas, error-handling contracts, and versioning strategies.
Experience in a cloud-native AWS/Azure environment, including serverless patterns (Lambda or similar), environment configuration and secrets management, and logging, metrics, and basic observability/debugging.
Strong software engineering fundamentals: Git, testing, code review, CI/CD-friendly patterns, and clean code practices.
Effective collaboration and communication skills, with the ability to work closely with product, and domain experts to converge on pragmatic, production-ready solutions.
Awareness of security, governance, and responsible AI in an enterprise context: RBAC and data access boundaries, PII and sensitive-data handling, and working within established platform guardrails and governance processes.
Demonstrated experience building on top of an existing platform/SDK (coding standards, templates, reusable components) rather than building custom platforms from scratch.
Familiarity with MLOps, data platforms, or observability tools used to track quality, performance, and usage of GenAI features.
Experience working with globally distributed teams in a client facing environment, especially across India/US time zones.
Evidence of being an early, high adopter of AI tools in your own workflow (code assistants, AI debuggers, documentation generators, experimentation tools).
Full Time
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Gurugram, Haryana
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