Founding AI Engineer

Product Engineer — Role Summary

We are looking for a Product Engineer to build user-facing products that combine AI capabilities with practical applications. You will work at the intersection of software engineering and AI, developing autonomous, agent-driven systems that solve complex educational and research problems.

Key Responsibilities:

• Product Development: Design, build, and deploy production-ready applications powered by LLMs and AI agents.
• Data Engineering: Build scalable ETL/ELT pipelines to process structured and unstructured data, including text and audio, for RAG and model fine-tuning.
• Agentic Workflows: Develop multi-step AI agents with tool calling, APIs, databases, search, reasoning, and memory.
• Rapid Prototyping: Turn ideas and research concepts into interactive, production-ready applications.
• AI Integration: Use frameworks such as LangChain, LlamaIndex, AutoGen, or custom orchestrators to integrate AI into scalable systems.
• User Experience: Transform raw AI outputs into reliable, intuitive, and responsive user experiences.
• Collaboration: Work closely with ML researchers and data engineers to integrate custom and fine-tuned models.
• Observability: Monitor agent behavior, manage edge cases, reduce hallucinations, and improve reliability in production.

The ideal candidate combines strong software engineering, AI/LLM expertise, data engineering, and product thinking, with the ability to take an AI concept from prototype to production.

About Asha Health

Asha Health helps medical practices launch their own AI clinics. We're backed by Y Combinator, General Catalyst, 186 Ventures, Reach Capital and many more. We recently raised an oversubscribed seed round from some of the best investors in Silicon Valley. Our team includes AI product leaders from companies like Google, physician executives from major health systems, and more.

About the Role

We're looking for a top 0.01% Software Engineer to join our engineering team in our Bangalore office.

4.6 fundamentally changed the game, which means that high intelligence, high agency engineers can now do the work of 10+ good engineers. It doesn't make sense to have anyone but the best on the team.

Since low level coding has become easier, what we expect from engineers on our team has expanded. Engineers on our team are expected to:

• Ship features end to end at a rapid pace
• Deeply research the domain and be their own product managers
• Ensure reliability and quality is best-in-class
• Design robust eng architecture, and develop testing and observability tools for each feature pre-launch
• Build each feature with deep customer empathy, meaning planning out and building stellar UX yourself

This means to thrive in a startup environment like ours, you not only need to be a stellar engineer, but you need to:

1. Be super adept with AI development tools and building the AI systems that build your features for you (Conductor, Browser agents, QA agents, Ralph loops, adverserial agents, and more).
2. Have exceptional product and UX taste, meaning you can ship features that are more effective than those historically designed by teams of product managers and designers.
3. Take the highest level of ownership around feature outcomes, reliability, and observability.

Other Points to Note

1. We are growing rapidly, our work has impact on tens of thousands of patients if not more.
2. On our team, everything you do is on the bleeding edge of applied AI.
3. We expect a high level of commitment from everyone on the team, most folks work 6 days and lead every project with intensity. It's a high ask, and we only bring on the best people. We compensate significantly above market, accordingly.

The role

A working product and a working deployment are two different things. You are the person who closes that gap.

You sit inside the client's head office. You get the deployment live, you get their teams using the dashboards, and you own whether the AI is returning something worth acting on. Every client is different: different languages on the floor, different store noise, different vocabulary for the same product, different CRM, different idea of what a good conversation looks like. The core platform does not change for each of them. You are the layer that makes it fit, and you are the one the client meets.

You are encouraged to spend time in stores. The engineers who do the best work here are the ones who have stood on a shop floor and watched where the pitch and the pipeline actually break. Nobody will make you go. You will also spend real time in the codebase, because you fix what you find rather than filing it.

What you build has a commercial edge to it. A pilot converts when the client sees the result they were promised, and an account grows when a second team inside it sees what is already sitting in their data. Both of those outcomes are yours to deliver, not somebody else's to chase.

How you will work

You design the deployment, you build it, and you own whether it holds up on a Saturday evening in a crowded store. Nobody hands you the plan, and nobody hands you the spec. You write both.

The decisions are yours. Which integration is worth the week, which vertical taxonomy needs building, what ships in the pilot and what waits, and when to tell a client that the thing they are asking for is the wrong thing to build. You go and find out what a client needs before anyone writes a line of code.

You will not be doing it alone. There are founders, AI engineers and product people around you, and they will build alongside you. What nobody will do is tell you what the client needs. That call is yours.

The work compounds if you do it well. What you learn on one deployment becomes a specification, then code, then a pattern the next one starts from. A year in, the deployments you designed should be running without you, and a new client should take a fraction of the time the first one did.

What you will do

Own the deployment end to end. Device provisioning, store connectivity, data flowing, first insight in front of the client. Get from kickoff to something real inside

the pilot window, and know by the halfway mark whether it is in trouble.

Get their HQ using it. A dashboard nobody opens is a failed deployment. Sit with the sales, marketing and L&D teams, show them what is in their own data, and

make sure the people who asked for this are actually looking at it every week.

Make the AI work on their floor. Their languages, their store noise, their product vocabulary. Benchmark transcription and speaker separation on their actual

audio, and fix what fails instead of explaining it away.

Build the vertical. Intent taxonomies, objection maps and prompt libraries for the category you are deployed into. A jewellery floor and an electronics floor do not

share a conversation model.

Wire it into their systems. CRM and POS integrations, so conversation data connects to what actually got sold and the insight can be checked against reality.

Build what the client asks for. Custom reports, dashboards and agents. Ground everything in source conversations and verify it before it ships, because a confident

wrong number costs an account.

Close the pilot. A pilot converts on results, not on effort. Know what the client agreed to judge this on, work backwards from it, and make sure the output in front of

their leadership at the end is the thing they asked for.

Grow the account. The same intelligence is worth something to marketing, L&D and category teams inside the same client. Spot which of them would benefit, show them what is already in their data, and hand a real opening to the account team.

Push it back into the product. Turn one-off client work into something the platform does by default, so the next deployment starts further ahead than this one did.

What we are looking for

Must have

• 0 to 5 years of experience. A consulting internship or an analyst role is the closest match to what this job actually asks for, but we care more about what you can do than where you did it
• Coding ability, ideally Python. Degree, internship, first job or your own projects. What we want to see is something you built that other people actually used
• Excel or Sheets at a real working level. A lot of the first conversation with a client happens in a spreadsheet before it ever happens in a dashboard
• The ability to explain a complicated idea simply. You will be taking AI output to people who do not think about models, and the explanation matters as much as the result
• Comfort at the boundaries. APIs, data pipelines, some frontend, some hardware when a device misbehaves
• An eye for where a deployment turns into more business, and the willingness to raise it yourself
• Heavy hands-on LLM usage. Prompts, evaluations, retrieval, and a clear view on where these tools break
• Fluent English and Hindi. A third Indian language counts for a lot, since the useful conversations happen on store floors and not only in HQ meeting rooms
• The instinct to go and find out what a client needs rather than waiting to be told

Good to have

Speech or audio work. Transcription, diarization, voice activity detection, or

anything that survives noisy real-world recording

Embedded or IoT experience, on ESP32 or similar

SQL and experience building things customers actually look at

Side projects, hackathons or internships where you shipped without a spec

and it worked

Senior AI Engineer

Code Generation, Agent Architecture & LLM Systems

📍 Mumbai (On-site) | Full-time | 5+ years

About the Role:

Unico Connect is an AI-first technology partner that builds custom mobile, web, and AI products for clients across multiple geographies.

We are hiring a Senior AI Engineer for a dedicated client engagement focused on building an AI-powered application builder platform - a product where users describe software in plain English and the system generates, previews, and iteratively refines working code.

The mandatory requirement for this role is hands-on production experience shipping LLM-powered systems with agent architectures, with experience in code generation or developer tooling contexts a strong advantage.

The role is product-focused and deeply hands-on. You will own everything between the user's prompt and correct code landing in the project: the agentic loop, code generation pipeline, context management, evaluation suite, and model cost strategy.

You will work alongside the Senior MLOps Engineer who operationalises the infrastructure around your system, and collaborate closely with backend, frontend, and DevOps engineers.

Responsibilities:

Agent Architecture

Design and own the agentic loop for the platform - request interpretation, planning, tool-calling sequence (read file, edit file, run build, search code, install package), and stop conditions.

Make and revisit architectural decisions on single-agent vs. multi-agent designs, including planner/executor splits and dedicated build-repair sub-agents.

Code Generation Pipeline

Own the end-to-end generation flow: task classification, context gathering, planning, targeted edits, verification, and commit.

Implement diff/search-replace-based file editing with fuzzy matching and fallback strategies.

Enforce scope discipline so the agent makes minimal diffs and does not modify code it was not asked to touch.

Self-Repair Loop

Build and tune the automated repair loop that pipes compiler, lint, build, and runtime errors back to the model with retry budgets and model escalation.

This loop is the primary quality lever - the difference between 60-70% and 90%+ build success rates.

Context Management

Build file-relevance retrieval so the agent sees the right files, not the whole codebase: dependency graphs, AST/tree-sitter-based chunking, embeddings, recency signals, and hybrid retrieval.

Implement conversation summarisation and memory for long sessions, and address long-project degradation through codebase summaries and periodic consistency passes.

Own token budgeting and prompt caching strategy.

Prompt Engineering as a Discipline

Own the system prompt and per-task prompt variants (new feature, bug fix, styling change).

Maintain few-shot examples and enforce coding conventions, stack rules, and prohibited behaviours such as no hardcoded secrets and no whole-file rewrites.

Version prompts like code with changelogs and rollback capability.

Evaluation and Quality Measurement

Design and own the evaluation suite: representative test prompts run on every prompt and model change, scored on build success rate, instruction adherence, and output quality including LLM-as-judge and visual/screenshot checks where relevant.

Define regression gates that block quality-degrading changes from shipping.

Treat evals the way engineers treat automated testing: versioned, automated, and tracked over time.

This responsibility is non-negotiable at this level.

Model Strategy and Cost

Design model routing - cheap and fast models for classification and small edits, frontier models for complex generation.

Drive cost optimisation through prompt caching, diff-based edits over full-file rewrites, and tighter context selection.

Track cost per agent run and tokens per task; evaluate new model releases against the eval suite and lead migrations when results justify it.

Safety and Reliability of Agent Behaviour

Defend against prompt injection from user content and fetched web content.

Ensure secrets never appear in generated client code.

Define what the agent's tools may and may not do in collaboration with the platform team.

Contribute to output moderation and abuse-pattern awareness.

Mentorship and Engineering Standards

Run code reviews, define engineering conventions for AI work, and raise the engineering bar across the AI team.

Work closely with the Senior MLOps Engineer on handoff of eval design, prompt configurations, and model routing logic.

Requirements:

Hands-on Production Ownership of LLM-Powered Systems with Agent Architectures (Mandatory)

Must have personally shipped and operated at least one complex production AI system - agentic, multi-step, or code generation - with end-to-end ownership of architecture, evaluation, and cost.

POCs, internal demos, and tutorial-grade work do not qualify.

5+ Years of Professional Software or AI Engineering Experience

With at least 3 years focused on LLM applications, AI engineering, or production AI systems.

Candidates with strong backend backgrounds and a clear, substantive pivot into LLM systems qualify.

Strong Python Proficiency and Service Development

Production-grade Python with FastAPI or equivalent: type hints, async patterns, streaming responses, testing, and packaging.

Not notebook-only.

Depth Across LLM APIs and Agent Systems

Production experience with at least two of OpenAI, Anthropic Claude, Google Gemini, or open-weight models (vLLM, Ollama, Together).

Production experience with at least one agent framework (LangGraph, CrewAI, AutoGen, LlamaIndex Agents) or hand-rolled equivalent.

Hands-on with tool calling, structured outputs, and multi-step reasoning.

Demonstrated, Systematic Evaluation Practice - Non-Negotiable

Must have built evaluation harnesses that gate production releases, not ad-hoc testing.

Hands-on with at least one of LangSmith, Langfuse, Promptfoo, Ragas, or DeepEval.

Candidates with no systematic answer to evaluation should not be considered at senior level regardless of other strengths.

Cost Discipline for Production AI

Track record of measurable cost optimisation on production AI features.

Able to speak in specifics: cost per request, savings achieved through caching or model routing, context reduction decisions.

AWS Working Knowledge

Hands-on with EC2, S3, IAM, and Docker.

Comfort with CI/CD workflows and deploying AI services.

Awareness of LLM Security Failure Modes

Familiar with prompt injection patterns, understands that system prompt rules alone are insufficient, and has experience with output validation and content safety in production.

Nice to Have

• Experience with AST/tree-sitter tooling, diff-based editing systems, or compiler-adjacent work
• MCP server authoring
• Open-source AI contributions
• Published technical writing on LLM systems
• Multi-modal model experience
• Fine-tuning exposure (LoRA, QLoRA, PEFT)

About Venxr

VenXR is an AI-driven AdTech startup building AdsGPT, redefining how brands plan, execute, and optimize ad campaigns through AI. We're a fast-growing, lean team looking for a Product Engineer who can own features end-to-end, from idea to shipped product.

About the Role

We're looking for a Product Engineer who thinks like a builder, not just a coder. Someone who cares as much about why we're building something as how it's built, and takes real ownership of the product outcomes, not just the code they ship.

Tech Stack

Frontend: React.js | Backend: Python (Django) | AI: Claude / LLM-driven development

Roles & Responsibilities

• End-to-End Feature Ownership
• Own features from concept to deployment — writing clean, scalable code while staying closely tied to the product goal and user impact behind it.
• Full-Stack Development
• Build and maintain front-end interfaces in React.js and back-end systems in Python/Django, ensuring performance, reliability, and scalability across the product.
• Product Thinking
• Work closely with Product, Design, and Founders to shape specs, question assumptions, and suggest simpler or smarter ways to solve user problems.
• Rapid Iteration
• Ship fast, test in production, gather feedback, and iterate, balancing speed with code quality.
• Cross-Functional Collaboration
• Partner with Design and Data teams to bring dashboards, AI-driven features, and data visualizations to life.
• System Design & Architecture
• Contribute to technical architecture decisions, ensuring the codebase stays scalable as the product and team grow.
• Debugging & Problem Solving
• Diagnose and resolve complex technical issues quickly, treating downtime or bugs as urgent, customer-impacting problems.
• AI-Native Development
• Actively build with AI — using Claude and other LLM tools not just to assist coding, but to develop AI-powered features, prompt workflows, and intelligent product capabilities within AdsGPT itself.

Requirements

• 3–5 years of experience as a Product Engineer, ideally in a startup or B2B SaaS environment
• Strong proficiency in React.js for front-end development
• Strong proficiency in Python, with hands-on experience in Django for back-end development
• Solid understanding of REST APIs, databases (SQL/NoSQL), and system design fundamentals
• Hands-on experience building with AI tools (like Claude), not just using them for coding assistance, but developing core AI products.
• Experience working directly with Product/Design teams, not just executing tickets
• Comfortable working in ambiguous, fast-changing environments with minimal hand-holding
• Strong debugging and problem-solving skills

Good to Have

• Familiarity with the Model Context Protocol (MCP) for securely connecting LLMs to external tools and APIs.
• Experience building conversational AI interfaces or managing complex multi-turn chatbot state across various platforms and channels.
• Experience in AdTech, MarTech, or data-heavy SaaS platforms.
• Exposure to cloud infra (AWS/GCP/Azure), Docker, and CI/CD pipelines.
• Experience with LLM evaluation/monitoring tools (Ragas, LangSmith, Langfuse, or similar).
• Experience with data visualization libraries (Recharts, D3.js, Chart.js).
• Familiarity with compliance-aware or explainable AI design in regulated domains.

What We Look For In Candidates

• Customer Obsession – A genuine curiosity about how your code impacts the customer's experience, not just whether it compiles.
• Ownership – You drive features to completion, chasing down blockers instead of waiting on them.
• Stakeholder Understanding & Management – You know when to loop in Product, Design, or Business to unblock yourself fast.
• High Standards – You hold your own code to a higher bar, even when no one's checking.
• Disagree and Commit – You debate technical decisions hard, then commit fully once the call is made.
• Simplify Relentlessly – You reach for the simplest working solution, not the most "clever" one.
• Solidarity with the Team – You show up for your teammates, no invitation needed.
• Vitality – You bring real energy and resilience that keeps you (and others) going.

• Why Join Venxr?
• Be part of a fast-growing startup that puts data at the heart of every decision.
• Opportunity to work on high-impact, real-world business challenges.
• Collaborative, transparent, and learning-oriented work environment.
• Flexible work culture with a strong focus on career development.
• Comprehensive health benefits to support your well-being.
• Team offsites and engagement activities to build stronger connections beyond work.
• Performance-based incentives and rewards recognizing your contribution and impact.

Job Title : Full-Stack AI Product Engineer

Experience : 3+ Years

Work Mode : Remote

Time Zone : IST

Engagement Type : Freelance

Availability : Must be comfortable attending US client calls during IST night hours.

Role Overview :

We are looking for a Full-Stack AI Product Engineer with hands-on experience building and deploying production-ready AI-powered applications. The ideal candidate should have strong Python backend development skills, practical expertise in LLMs, AI agents, RAG pipelines and tool calling, along with basic frontend development knowledge.

Mandatory Skills :

Python, FastAPI, AI / LLMs, AI Agents, RAG, Tool Calling, PostgreSQL, Redis, Docker, GCP / Cloud Run, CI/CD, Basic React.js / Next.js.

Key Responsibilities :

• Design, develop and maintain AI-powered products using Python and FastAPI.
• Build and integrate LLM-based applications, AI agents, RAG pipelines and tool-calling workflows.
• Develop scalable backend services and REST APIs.
• Work with PostgreSQL and Redis for data storage, caching and performance optimization.
• Containerize applications using Docker and deploy services on GCP, preferably Cloud Run.
• Implement CI/CD pipelines and follow production deployment best practices.
• Develop and integrate basic frontend features using React.js or Next.js.
• Troubleshoot production issues, optimize performance and ensure application reliability.
• Collaborate with US-based clients and stakeholders during IST night hours when required.

Required Skills :

• 3+ years of relevant software development experience.
• Strong proficiency in Python and FastAPI.
• Hands-on experience with AI / LLMs and production-level AI product development.
• Practical experience building AI agents, RAG pipelines and tool / function-calling workflows.
• Good knowledge of PostgreSQL and Redis.
• Experience with Docker, GCP / Cloud Run and CI/CD pipelines.
• Basic working knowledge of React.js or Next.js.
• Understanding of REST APIs, application architecture, deployment and production debugging.
• Excellent problem-solving and communication skills.

Preferred Qualifications :

• Experience deploying and maintaining AI applications in production.
• Familiarity with LLM orchestration frameworks and vector databases.
• Experience with cloud-native application development and monitoring.

Important Requirements :

• Must have demonstrable, hands-on experience delivering production-grade AI products.
• Must be comfortable working remotely and coordinating with US clients during IST night hours.

AI Product Builder | Consumer Experiences

TL;DR: ₹1 lakh/month. HSR Layout, Bengaluru. Full-time, in-person preferred, with flexibility for an exceptional fit. Use AI to build and refine consumer experiences. No CV or prior experience required. Apply by email, then an unpaid 48-hour workout assignment.

I'm Vishnu, building Aira. AI is the biggest equaliser we have, but nobody has figured out the consumer use case yet. I think Aira will be the reason everyday people start using AI daily.

I don't believe chatting is the final interface for AI. So Aira is built around a hand-drawn character that feels alive, shapes what's on your screen, and brings voice, animation and music together. The goal is the best answer, delivered in the best way for your situation, with help acting on it.

Say you're deciding what to cook. Aira helps you pick a meal, shows the ingredients, arranges the purchase with your confirmation. While you cook, it keeps the recipe and your current step on screen, speaks short instructions, and waits while your hands are busy. The same screen won't work at the gym, in meditation or while fixing a tyre. The conversation, the actions and what you see and hear should feel like one experience that adapts to what you're doing.

I'm hiring people who would have wanted to work on the first iPhone. People who obsessed over the small details that made something unfamiliar feel natural, and cared about what a person felt the first time they used it.

We've built the foundation. Your job is to take an experience, decide how it should work, build it with AI, and keep agonising until it feels right. In practice: tuning prompts and behaviour files, building or changing screen components with AI, and testing what you've made.

You may not yet be able to explain why an interaction feels incomplete. I want you to follow that feeling and build a better version. That's why we don't ask for a CV, prior experience or a coding background. We need product thinking, creativity and ownership — you question what AI gives you, make it work, and keep improving it.

We're launching in San Francisco this month. You'll work directly with me and help shape what people experience the first time they meet Aira.

The process

You'll build a workout experience from a minimal starter. We give you a stepper, a timer and the component plumbing; you decide how it behaves and build the rest. Anyone can attempt it, before any screening. You have 48 hours after receiving it. It's unpaid, for evaluation only, and your work stays yours.

AI helps me review, but I pick the shortlist myself. Shortlisted candidates get a 30-minute conversation. I aim to reply within 24 hours of submission and finish within a week.

About Venxr

VenXR is an AI-driven AdTech startup building AdsGPT, redefining how brands plan, execute, and optimize ad campaigns through AI. We're a fast-growing, lean team looking for a Product Engineer who can own features end-to-end, from idea to shipped product.

About the Role

We're looking for a Product Engineer who thinks like a builder, not just a coder. Someone who cares as much about why we're building something as how it's built, and takes real ownership of the product outcomes, not just the code they ship.

Tech Stack

Frontend: React.js | Backend: Python (Django) | AI: Claude / LLM-driven development

Roles & Responsibilities

• End-to-End Feature Ownership
• Own features from concept to deployment — writing clean, scalable code while staying closely tied to the product goal and user impact behind it.
• Full-Stack Development
• Build and maintain front-end interfaces in React.js and back-end systems in Python/Django, ensuring performance, reliability, and scalability across the product.
• Product Thinking
• Work closely with Product, Design, and Founders to shape specs, question assumptions, and suggest simpler or smarter ways to solve user problems.
• Rapid Iteration
• Ship fast, test in production, gather feedback, and iterate, balancing speed with code quality.
• Cross-Functional Collaboration
• Partner with Design and Data teams to bring dashboards, AI-driven features, and data visualizations to life.
• System Design & Architecture
• Contribute to technical architecture decisions, ensuring the codebase stays scalable as the product and team grow.
• Debugging & Problem Solving
• Diagnose and resolve complex technical issues quickly, treating downtime or bugs as urgent, customer-impacting problems.
• AI-Native Development
• Actively build with AI — using Claude and other LLM tools not just to assist coding, but to develop AI-powered features, prompt workflows, and intelligent product capabilities within AdsGPT itself.

Requirements

• 1-2 years of experience as a Product Engineer, ideally in a startup or B2B SaaS environment
• Candidates with B.Tech in CSE, AI/ML or any other equivalent educational background from Tier-1 and Tier-2 colleges will be preferred
• Strong proficiency in React.js for front-end development
• Strong proficiency in Python, with hands-on experience in Django for back-end development
• Solid understanding of REST APIs, databases (SQL/NoSQL), and system design fundamentals
• Hands-on experience building with AI tools (like Claude), not just using them for coding assistance, but developing core AI products.
• Experience working directly with Product/Design teams, not just executing tickets
• Comfortable working in ambiguous, fast-changing environments with minimal hand-holding
• Strong debugging and problem-solving skills

Good to Have

• Familiarity with the Model Context Protocol (MCP) for securely connecting LLMs to external tools and APIs.
• Experience building conversational AI interfaces or managing complex multi-turn chatbot state across various platforms and channels.
• Experience in AdTech, MarTech, or data-heavy SaaS platforms.
• Exposure to cloud infra (AWS/GCP/Azure), Docker, and CI/CD pipelines.
• Experience with LLM evaluation/monitoring tools (Ragas, LangSmith, Langfuse, or similar).
• Experience with data visualization libraries (Recharts, D3.js, Chart.js).
• Familiarity with compliance-aware or explainable AI design in regulated domains.

What We Look For In Candidates

• Customer Obsession – A genuine curiosity about how your code impacts the customer's experience, not just whether it compiles.
• Ownership – You drive features to completion, chasing down blockers instead of waiting on them.
• Stakeholder Understanding & Management – You know when to loop in Product, Design, or Business to unblock yourself fast.
• High Standards – You hold your own code to a higher bar, even when no one's checking.
• Disagree and Commit – You debate technical decisions hard, then commit fully once the call is made.
• Simplify Relentlessly – You reach for the simplest working solution, not the most "clever" one.
• Solidarity with the Team – You show up for your teammates, no invitation needed.
• Vitality – You bring real energy and resilience that keeps you (and others) going.

• Why Join Venxr?
• Be part of a fast-growing startup that puts data at the heart of every decision.
• Opportunity to work on high-impact, real-world business challenges.
• Collaborative, transparent, and learning-oriented work environment.
• Flexible work culture with a strong focus on career development.
• Comprehensive health benefits to support your well-being.
• Team offsites and engagement activities to build stronger connections beyond work.
• Performance-based incentives and rewards recognizing your contribution and impact.

Job Overview

We are looking for an experienced AI/ML Engineer with strong full-stack development skills to design, develop, and deploy AI-powered applications. The ideal candidate should be proficient in Python backend development, frontend technologies, and LLM integration, with the ability to build end-to-end AI-native products, custom dashboards, and lightweight applications.

This role focuses on applying AI to real-world business problems through rapid prototyping, prompt engineering, Retrieval-Augmented Generation (RAG), and scalable application development.

Key Responsibilities

• Design, develop, and deploy end-to-end AI-powered applications.
• Build backend services and REST APIs using Python.
• Develop responsive frontends using React, Next.js, or similar frameworks.
• Integrate LLM APIs such as OpenAI, Claude, and Hugging Face into production applications.
• Build custom dashboards, internal tools, micro-apps, and AI-driven business solutions.
• Implement prompt engineering, embeddings, RAG pipelines, and context-aware AI systems.
• Rapidly prototype and validate ideas using modern AI-assisted development workflows.
• Optimize application performance, scalability, reliability, and AI usage costs.
• Work with data processing libraries such as Pandas and NumPy.
• Collaborate with product and business teams to translate requirements into functional AI solutions.
• Take ownership of features from ideation and development through testing, deployment, and production support.

Required Skills & Qualifications

• 2+ years of experience in software engineering, AI engineering, or application development.
• Strong proficiency in Python and backend development.
• Hands-on experience with React, Next.js, or similar frontend technologies.
• Practical experience integrating LLM APIs such as OpenAI, Claude, or Hugging Face.
• Understanding of REST APIs, microservices, and system design principles.
• Knowledge of prompt engineering, embeddings, and RAG architectures.
• Experience with data processing using Pandas and NumPy.
• Ability to independently build, test, and deploy full-stack applications.
• Strong problem-solving, debugging, and analytical skills.
• Ability to work in a fast-paced environment with a strong ownership mindset.

Good to Have

• Experience with LangChain, LlamaIndex, or similar AI frameworks.
• Familiarity with vector databases such as Pinecone, Weaviate, or Supabase.
• Experience building AI chat interfaces, copilots, assistants, and custom dashboards.
• Familiarity with cloud platforms, application deployment, and production monitoring.
• Experience with AI-assisted coding tools and rapid prototyping workflows.

What We Are Looking For

• A full-stack AI engineer who is comfortable working across both backend and frontend development.
• Someone focused on building practical AI-powered products rather than only training machine learning models.
• A developer who can prototype quickly, validate ideas, and iterate efficiently using a “vibe coding” approach.
• A self-driven professional who can take ownership of a feature from concept to production.

Please note that this job is only for Ahmedabad location.

Job Description-

Location- Bengaluru

Experience-5-7 years

Wissen is building the engineering team for RES Modernization, a corporate services programme at a leading global financial services firm, rebuilding the lease and real estate management platform on a cloud stack with AI and agentic capability designed in rather than bolted on. The AI Solution Engineer builds that product: .NET services and APIs at the core, Python wherever the AI, retrieval and data work lives. We want a strong .NET engineer who has moved into Python, not a Python specialist who once touched C#.

• Build features end to end on a .NET codebase: C# and ASP.NET Core services, the APIs above them, and the React or Angular front end where a feature needs one.
• Write the Python that carries the AI work — retrieval and grounding, prompt and agent orchestration, evaluation harnesses, and the pipelines that feed them.
• Put LLM and agentic capability into real product workflows, with the grounding, guardrails and cost and latency budgets a regulated environment demands.
• Take your work through the client's engineering and change review: design notes, test evidence, security and code-quality gates.
• Work with the stream's data and integration engineers on the Snowflake and integration layer the product reads from.