AI/Ml Engineer (Python, React)

• *Role \& Responsibilities**

Responsibilities

• Business: Immerse in operations until you think like an insider.

Rapidly acquire domain expertise through direct observation, translate between

business and engineering seamlessly, and mentor engineers in your area on

immersion. Influence senior stakeholders effectively, manage complex stakeholder

landscapes with competing agendas, and build trust rapidly with new stakeholders.

• Delivery: Lead rapid delivery initiatives across teams in your area, coach on

prototype\-first approaches, and establish trust through consistent fast delivery.

Build complete applications rapidly across any technology stack, select the right

tools for each problem, and define clear criteria for prototype\-to\-production

transitions.

• Generative AI: Architect RAG systems for complex use cases across teams,

implement advanced techniques (hybrid search, reranking, query expansion),

mentor engineers on RAG best practices, and establish RAG standards. Lead

evaluation strategy across teams, establishing annotation guidelines, training

human\-calibrated LLM judges, and building evaluation pipelines that connect

tracing to datasets to experiments.

• People: Build high\-performing teams across your area, navigate complex

interpersonal dynamics, foster psychological safety, and create environments

where diverse perspectives are valued. Influence through communication at all

levels — from frontline to executive. Handle difficult conversations skilfully and train

engineers in your area on effective communication.

• AI\-Augmented Development: Optimise AI tool usage across teams in your area,

train engineers on AI\-augmented and agentic engineering workflows, evaluate new

AI development tools, and establish practices that balance AI speed with

verification rigour.

• Scale: Design complex multi\-component systems end\-to\-end, evaluate

architectural options for large initiatives across teams, guide technical decisions for

your area, and mentor engineers on architecture. Create debt reduction strategies

across teams, influence roadmap decisions to include debt work, and teach

engineers when to accept debt for speed versus when to invest in quality.

• Documentation: Define documentation standards across teams in your area,

create documentation systems and templates, train engineers on spec\-driven

development, and ensure documentation quality across projects. Lead pattern

generalization initiatives, defining criteria for when to generalize versus keep

custom.

• Reliability: Define reliability standards across teams in your area, drive post\-

incident improvements systematically, design capacity planning processes, and

mentor engineers on SRE practices.

• *Ideal Candidate**

• Strong Staff Software Engineer / FDE profile (full\-stack \+ production GenAI, multi\-team technical leadership)
• Mandatory (Experience 1\) – Must have 7\+ years of relevant professional software engineering experience, with demonstrated full\-stack delivery across backend and frontend.
• Mandatory (Experience 2\) – Must have deep production experience with Python AND JavaScript/TypeScript, working comfortably across the full stack.
• Mandatory (Experience 3\) – Must have 2\+ years of experience in generative AI applications developement — LLM integrations, vector databases, RAG systems, and evaluation pipelines
• Mandatory (Experience 4\) – Must have strong experience with modern frontend frameworks (Next.js / React) and backend API development.
• Mandatory (Experience 5\) – Must have extensive experience with cloud platforms (AWS preferred; Azure/GCP valued), including infrastructure\-as\-code (CloudFormation / Terraform).
• Mandatory (Experience 6\) – Must have working knowledge of multiple database paradigms — relational (PostgreSQL), document, and key\-value (Redis) — with ability to select the right storage per problem.
• Mandatory (Experience 7\) – Must have strong experience with CI/CD pipelines (e.g. GitHub Actions), containerization, and production deployment strategies.
• Mandatory (Experience 8\) – Must have demonstrable fluency with AI coding tools (Claude Code, Cursor, GitHub Copilot, or similar) and proven ability to design agentic engineering workflows and train teams on them

Pay: ₹393,473\.71 \- ₹4,500,000\.00 per year

Application Question(s)

• Which frontend frameworks have you shipped to production?
• Which cloud platforms have you worked on, and have you used IaC (Terraform/CloudFormation)?
• Which databases have you worked across?
• What is your notice period in days? (30\)
• What is your current CTC in LPA?
• What is your expected CTC in LPA? (45\)

Experience

• overall: 7 years (Required)

Work Location: Remote