Senior Data Scientist - AI/ML & Generative AI

About the Company

Techylla is a fast-growing IT and consulting firm delivering SAP/ERP, data engineering, AI/ML, and cloud solutions to clients across industries in the US and India. Our AI-ML advisory practice sits alongside deep SAP, data engineering, and full-stack capabilities — which means the data scientists here don't work in a silo; they build alongside engineers who already own the client's data, systems, and integration layer.

About the Role

We're scaling our AI/ML and Generative AI practice from the ground up — and we're looking for a senior data scientist who wants to own that build, not just staff it. This is a senior, hands-on individual contributor role with real scope: you'll define how Techylla approaches applied ML and GenAI work for clients, build the first reference solutions yourself, and mentor the people who join the practice after you. You'll split your time roughly between deep technical build work, client-facing solutioning, and coaching junior team members — and you'll have a direct line to leadership on where the practice goes next.

Responsibilities

• Own AI/ML and GenAI strategy for a fast-scaling consulting practice — not just execute someone else's roadmap.
• Work directly with leadership on client-facing solutions, from proof-of-concept to production, across a range of industries.
• Shape how Techylla builds and governs LLM-based and applied ML solutions from day one — your technical choices become the team's standards.
• High-impact, high-visibility role in a lean team where your work reaches clients in weeks, not quarters.
• Direct exposure to leadership and to the commercial side of the business — you'll see how your technical work translates into client wins.
• Room to grow into a practice-lead or head-of-AI role as the team scales.
• Design, build, and deploy advanced ML/DL models — including sequence models (LSTM/RNN, transformers) and reinforcement learning (Q-learning, SARSA, or modern policy-gradient methods) — to solve high-value business problems.
• Own model selection, experimentation, and evaluation, and make the call on when a simpler model beats a more complex one.
• Build reusable model components and internal accelerators that speed up delivery on future client engagements.
• Lead applied Generative AI initiatives end-to-end: RAG pipelines, LLM fine-tuning, prompt engineering, agentic workflows, and evaluation frameworks.
• Evaluate and select LLM providers, frameworks, and hosting approaches (proprietary vs. open-source, cloud vs. self-hosted) based on client cost and data-governance constraints.
• Build guardrails, evaluation harnesses, and monitoring so GenAI solutions are safe and reliable enough for production use, not just demos.
• Drive NLP solutions for unstructured enterprise data — including entity extraction, sentiment analysis, classification, and document automation — that eliminate manual work at scale.
• Work with messy, real-world enterprise data (inconsistent formats, legacy systems, multiple languages where relevant) and design pipelines that hold up in production.
• Architect scalable, production-grade AI/ML pipelines on AWS and/or Microsoft Azure.
• Set up MLOps practices — versioning, monitoring, retraining triggers, cost tracking — so models don't quietly degrade after go-live.
• Collaborate with Techylla's data engineering and cloud/integration teams so AI/ML solutions plug cleanly into existing client systems (SAP, BTP, and others).
• Partner with engineering, product, and delivery teams to translate ambiguous business problems into shippable data science solutions clients actually use.
• Present findings, trade-offs, and recommendations directly to client stakeholders and internal leadership — in language non-technical stakeholders can act on.
• Scope and estimate AI/ML work for proposals and pre-sales conversations alongside the delivery and leadership teams.
• Mentor junior data scientists and set the bar for code quality, experimentation discipline, model governance, and responsible AI practices.
• Contribute to hiring — interviewing and evaluating future data science and ML hires as the practice grows.

Qualifications

• Master's degree or higher in Data Science, Computer Science, Engineering, or a related quantitative field.
• Comfortable communicating technical trade-offs to non-technical stakeholders, including clients.
• Self-directed — able to scope and drive work with real ambiguity, typical of an early-stage practice rather than a mature team with established processes.

Nice to Have

• Prior experience in an IT services or consulting environment supporting multiple clients concurrently.
• Exposure to regulated or compliance-heavy industries (e.g., healthcare, finance, manufacturing).
• Publications, patents, or open-source contributions in ML/AI.
• Experience with MLOps tooling (MLflow, SageMaker, Azure ML).
• AWS or Azure cloud certification(s).
• Exposure to SAP, BTP, or enterprise integration landscapes.

Tools & Technologies

• Languages: Python, SQL
• ML/DL Frameworks: PyTorch, TensorFlow
• Cloud & Data Platforms: AWS, Microsoft Azure, Hadoop, Spark
• GenAI/LLM Tooling: LangChain, LlamaIndex, or equivalent RAG/agent frameworks
• MLOps: MLflow, SageMaker, or Azure ML (nice to have)

Compensation & Benefits

• Competitive compensation, commensurate with experience.
• Flexible, hybrid/remote-friendly working arrangement across India and the US.
• Direct mentorship and exposure to leadership decision-making.
• Genuine ownership of a growing practice area, with room to shape your own scope as it expands.

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