Data Scientist

Aligned Automation; Maharashtra; On-site; INR 400,000 - 700,000; Full time

Job summary

Aligned Automation in Maharashtra is seeking a Senior AI Engineer to design and deliver GenAI and agentic systems. You will lead end-to-end development of LLM-powered applications, from problem framing to production deployment, and act as a technical anchor for the team.

The role requires 7–10 years of hands-on data science experience with production GenAI projects, strong Python/SQL, and expertise in ML, RAG, and tool-using agents, with mentoring responsibilities.

Qualifications

• 7–10 years in data science / ML with 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.

Responsibilities

• 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.

Skills

GenAI applications

LangChain/LangGraph

Python (Pandas, NumPy)

SQL

PyTorch

RAG systems

Agentic workflows

LLM evaluation

Production engineering

Mentoring/leading

Tools

Docker

Kubernetes

Terraform

LangSmith

Langfuse

Arize

LlamaIndex

CrewAI

AutoGen

Job description

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.

About the Job

About Aligned Automation

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.

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: 7–10 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

• 7–10 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.