Senior Data Scientist

Aligned Automation; Pune District; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Aligned Automation in Pune District is hiring a Senior AI Engineer to lead the design and delivery of LLM-powered applications. The ideal candidate possesses a strong data science foundation, with 8-12 years of experience in building GenAI and agentic systems.

The role includes responsibility for architecture decisions and mentoring data scientists. Aligned Automation promotes a culture of Care, Courage, Curiosity, and Collaboration, ensuring a supportive environment for innovation.

Qualifications

• 8–12 years in data science / ML with production‑deployed models.
• Strong foundations in supervised/unsupervised learning.
• Hands-on experience designing agentic systems.

Responsibilities

• Design and build end-to-end GenAI applications.
• Establish evaluation frameworks for AI systems.
• Mentor mid-level data scientists and engineers.

Skills

Machine Learning and statistics

GenAI applications development

Python (pandas, NumPy, scikit-learn)

SQL

PyTorch or equivalent DL framework

API design and production engineering

Tools

Docker

CI/CD

Kubernetes

Job description

Aligned Automation Services Pvt Ltd | Full time

At Aligned Automation, we live by our "Better Together" philosophy to build a better world. As a strategic service provider to Fortune 500 companies, we help digitize enterprise operations and drive impactful business strategies. Our purpose goes beyond projects—we strive to deliver meaningful, sustainable change that shapes a more optimistic and equitable future.

Our culture is deeply rooted in our 4Cs—Care, Courage, Curiosity, and Collaboration—ensuring that each employee is empowered to grow, innovate, and thrive in an inclusive workplace.

Senior AI Engineer / Data Scientist — GenAI & Agentic Systems

About the Role

We're looking for a Senior AI Engineer with a strong data science foundation who has made the transition into building production GenAI and agentic systems. You'll lead the design and delivery of LLM-powered applications end to end — from problem framing through deployment — and serve as a technical anchor for the team. This is a hands‑on senior role with significant influence over architecture, standards, and how we apply AI to real business problems.

Experience:

8–12 years, including demonstrable hands‑on experience shipping GenAI/agentic applications to production.

What You’ll Do

• Design and build end-to-end GenAI applications: RAG systems, agentic workflows, tool‑using assistants, and multi‑agent orchestration.
• Own architecture decisions across model selection, retrieval design, orchestration, and serving — balancing cost, latency, and quality.
• Establish evaluation frameworks, observability, and guardrails to make AI systems reliable and safe in production.
• Partner with product and business stakeholders to translate ambiguous problems into scoped, high‑ROI AI solutions, and to judge when GenAI is (and isn’t) the right tool.
• Set technical standards, review designs and code, and mentor mid‑level data scientists and engineers.
• Stay current with a fast‑moving field and bring well‑filtered, practical recommendations to the team.

Required Qualifications

• 8–12 years in data science / ML, with a track record of production‑deployed models and systems.
• Strong foundations in ML and statistics: supervised/unsupervised learning, experimentation and A/B testing, model evaluation; familiarity with causal inference and time series.
• Expert Python (pandas, NumPy, scikit‑learn), strong SQL, and proficiency in PyTorch or an equivalent DL framework.
• Demonstrated experience building GenAI applications: prompt engineering, RAG (chunking, embeddings, vector databases, reranking), and fine‑tuning/adaptation (SFT, LoRA/PEFT) with sound judgment on when to use each.
• Hands‑on experience designing agentic systems: tool/function calling, planning and orchestration, memory and state management, using frameworks such as LangChain/LangGraph, LlamaIndex, CrewAI, or AutoGen (and knowing when to build directly).
• Experience with LLM evaluation and observability (e.g., LLM‑as‑judge, golden datasets, tracing tools like LangSmith/Langfuse/Arize) and with guardrails for hallucination, safety, and prompt‑injection defense.
• Production engineering skills: API design, serving and streaming, caching/batching, rate‑limit handling, Docker, and CI/CD.
• Strong communication skills and experience mentoring or leading technically.

Preferred Qualifications

• Inference optimization experience (quantization, vLLM/TGI, GPU serving) and cost optimization at scale.
• Familiarity with MCP and emerging model‑to‑system interoperability standards.
• Kubernetes and infrastructure‑as‑code (Terraform).
• Experience with multimodal or unstructured data pipelines.