Senior Technical Consultant - Data and AI

Roles and Responsibilities:

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. 

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.
• 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
• Hands-on experience with leading open source and major model providers such as LLama, Anthropic's Claude, Open AI. Etc.
• Solid understanding of transformer architectures, attention mechanisms, and other advanced deep learning concepts.
• Knowledge of generative AI concepts, including fine-tuning, transfer learning, and RAG methods.
• Experience with the machine learning lifecycle, including model deployment, monitoring, drift detection/retraining, and canary testing.
• Strong communication and collaboration skills, with the ability to present complex technical information to both technical and non-technical audiences.
• Experience in pre-sales activities, including project scoping, estimation, and solution design.

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. 

• 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. 

Additional / Preferred

• 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).