Product 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.

AI Engineer

LLMs, Agents & AI Services

📍 Mumbai (On-site) | Full-time | 2-4 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.

AI is core to how we design, deliver, and scale software for our customers.

We are hiring an AI Engineer for a dedicated client engagement building a complex production AI platform, working on the AI capabilities and agentic features at the core of the product.

The mandatory requirement for this role is at least one AI feature personally shipped to production for real users, with operational ownership.

The role suits someone who thinks quickly on solutioning, can take an ambiguous problem to a working prototype in days, and has the discipline to carry it through to production with predictable economics.

You will work alongside the Senior AI Engineer and the wider pod, with ownership of parts of the AI surface area of the product.

Responsibilities:

Solutioning and POCs

Translate ambiguous customer problems into working POCs at speed.

Pick the right model, framework, and architecture, and demonstrate value early before scaling investment.

LLM Application Development

Build AI features and services using LLM APIs from OpenAI, Anthropic, Google, and self-hosted open-weight models (Llama, Qwen, Mistral).

Choose the right model per use case based on cost, latency, capability, and context-window trade-offs.

Agentic System Design

Design and implement agentic workflows using LangGraph, CrewAI, AutoGen, LlamaIndex Agents, or custom orchestration.

Cover tool use, planning, memory, and multi-step reasoning appropriate to the problem.

API and Service Development

Build production AI services and APIs using Python and FastAPI.

Handle streaming responses, async processing, structured outputs, retries, and graceful degradation when models or tools fail.

Retrieval and Tool Integration

Implement RAG pipelines with vector databases (Pinecone, Weaviate, Qdrant, pgvector, Chroma), embeddings, chunking strategies, hybrid search, and reranking.

Integrate external tools, internal APIs, and document sources through tool-calling and MCP-style patterns.

Cost Analysis and Unit Economics

Model the per-request and per-user cost of every AI feature before it ships.

Track token usage, prompt caching, batching, and model-routing strategies.

Drive measurable improvements in unit economics.

Production Hardening

Add observability and tracing (LangSmith, Langfuse, OpenTelemetry), guardrails, content safety checks, prompt injection defences, and fallback behaviour.

Prompt Engineering and Evaluation

Design, test, and iterate prompts with measured outcomes.

Build evaluation harnesses for accuracy, hallucination, latency, and cost.

Run benchmarks across models and prompt variants before locking in a design.

Requirements:

AI Feature Shipped to Production (Mandatory)

Must have personally built and shipped at least one AI feature that runs in production for real users, with operational ownership.

POCs, internal demos, and one-off scripts do not qualify.

2 to 4 Years of Professional Software or AI Engineering Experience

With at least one production AI feature owned end to end.

Strong Python Proficiency and API Development with FastAPI

Comfort with type hints, async, packaging, testing, streaming responses, and authentication.

Production-grade Python, not notebook-only code.

Hands-on Depth Across the LLM and Agent Stack

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

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

Working knowledge of RAG, embeddings, and vector databases (Pinecone, Weaviate, Qdrant, pgvector, Chroma).

Solutioning Speed and POC Velocity

Demonstrated ability to move from a fuzzy problem to a working prototype in days.

Strong instinct for what to build first, what to defer, and what to throw away.

Cost Discipline for Production AI

Ability to calculate, monitor, and optimise the cost of LLM APIs, tokens, embeddings, vector store usage, and infrastructure.

Treats unit economics as a first-class concern.

AWS Familiarity

Working knowledge of EC2, S3, IAM, and at least one of Bedrock, SageMaker, or equivalent.

Comfortable in a Fast-Moving Environment

Self-directed, comfortable with ambiguity, takes ownership without being asked, and ships under shifting priorities.

Strong Written and Spoken English Communication

Able to explain trade-offs to non-AI engineers, designers, product managers, and clients in plain language.

Nice to Have

• fine-tuning or LoRA, QLoRA, PEFT exposure
• MCP server authoring
• eval framework experience (LangSmith, Promptfoo, Ragas, DeepEval)
• open-source AI contributions
• multi-modal models (vision, audio)

About Us

We believe the future of software development is AI-native — where engineers operate at a higher level of abstraction and quality remains non-negotiable. 

Incubyte is a software craft consultancy where the “how” of building software matters as much as the “what”.  

We partner with companies of all sizes, from helping enterprises build, scale, and modernize to early-stage founders bring their ideas to life. 

Our engineers operate in an AI-native development model, using AI as a collaborator across the SDLC to accelerate development while upholding the discipline of software craftsmanship. Guided by Software Craftsmanship and Extreme Programming practices, we build reliable, maintainable, and scalable systems with speed, without compromising quality. If this way of building software resonates with you, we’d like to talk. 

Our Guiding Principles 

These principles define how we work at Incubyte. They are non-negotiable. 

Relentless Pursuit of Quality with Pragmatism 

  We build high-quality systems without losing sight of delivery. 

Extreme Ownership 

  We take responsibility end-to-end for decisions, execution, and outcomes. 

Proactive Collaboration 

  We collaborate closely, challenge each other, and solve problems together. 

Active Pursuit of Mastery 

  We continuously improve our craft and raise our bar. 

Invite, Give, and Act on Feedback 

We seek, give, and act on feedback to get better every day. 

Ensuring Client Success 

We act as trusted partners and focus on real outcomes, not just output. 

Experience Level

This role is ideal for engineers with total 3+ years of experience with a proven track record of shipping complex projects successfully.

An experienced individual contributor and leader who thrives in large, complex projects with widespread impact.

What You’ll Do as a Software Craftsperson 

• Design and build high-quality, maintainable systems using disciplined engineering practices such as TDD, continuous refactoring, and pair programming 
• Operate in an AI-native development model, using AI as a collaborator to explore architecture and design, accelerate development, and continuously improve systems while applying strong judgment to ensure that speed never compromises quality 
• Take end-to-end ownership of outcomes from problem understanding and system design to implementation, deployment, and operation in production 
• Make thoughtful design decisions that balance simplicity, scalability, and long-term maintainability in real-world systems 
• Maintain a high bar for engineering quality through rigorous testing, code reviews, and continuous feedback 
• Investigate and resolve production issues, and implement systemic improvements to prevent recurrence 
• Work directly with clients, navigate ambiguity, and translate business problems into well-designed technical solutions 
• Contribute to improving team practices, tooling, and systems to raise the overall quality and effectiveness of engineering 

Requirements

What You’ll Bring 

• 3+ years of experience building high-quality, production systems (flexible based on demonstrated capability) 
• Strong fundamentals in software engineering, including object-oriented design, system design, and testing practices such as TDD 
• Demonstrated ability to build simple, maintainable, and scalable systems with a focus on long-term reliability 
• Proficiency in one or more modern technologies, Python, PHP, JavaScript, or TypeScript, with the ability to learn new technologies quickly 
• Deep experience working with Git in collaborative environments, including managing shared codebases, conducting code reviews, and maintaining a high bar for quality 
• Ability to operate effectively in an AI-native workflow using AI as a collaborator to explore solutions and accelerate development, while applying strong judgment to ensure correctness, quality, and maintainability 
• Clear thinking and strong problem-solving ability, with the capacity to break down complex problems into simple, well-structured solutions 
• A strong sense of ownership — you take responsibility for outcomes, care deeply about quality, and are not comfortable shipping work that does not meet your standards.

Benefits

Life at Incubyte 

We are a remote-first company with structured flexibility. Teams commit to shared rhythms during core hours, ensuring smooth collaboration while maintaining autonomy. Twice a year, we come together in person for a co-working sprint and once a year for a retreat - with all travel expenses covered. 

Our environment is built for crafters: pairing, refactoring, experimenting with AI, and pushing the boundaries of software excellence. We are all lifelong learners, and our work is our passion. 

Perks

• Dedicated learning & development budget. 
• Sponsorship for conference talks. 
• Comprehensive medical & term insurance. 
• Employee-friendly leave policies. 
• Home Office fund 
• Medical Insurance

Most sales tools help you send emails. We’re building something different.

At Salesforge, we’re creating autonomous AI agents that can:

Find the right prospects

Generate highly personalized outreach

Run conversations

And book meetings

All without human involvement.

Why this is interesting

A lot of AI products stop at “generate text.” We’re focused on outcomes.

That means solving problems like:

How do you generate messages that actually get replies?

How do you evaluate and improve agent performance over time?

How do you orchestrate millions of AI-driven interactions reliably?

How do you combine structured data + LLMs in a way that scales?

If you enjoy working at the intersection of systems + AI + real-world feedback loops, this will feel like a playground.

What you’ll be working on

You won’t be maintaining legacy systems.

You’ll be:

Designing and building core backend systems that power our AI agents

Creating APIs and services that handle high-scale, real-time workflows

Working with queues (Kafka / SQS / RabbitMQ) to orchestrate async systems

Thinking deeply about performance, cost, and reliability in AI pipelines

Shipping features end-to-end with a small, senior team

The team

We’re a small group of experienced builders. We move quickly, care about quality, and avoid unnecessary process.

No layers of management.

No long planning cycles.

Lots of ownership and autonomy.

What we’re looking for

5+ years of backend engineering experience

Strong system design fundamentals

Experience with distributed systems and async processing

Familiarity with relational and/or document databases

Clear communicator, low ego, high ownership

Why join

You’ll work on a product where the output is measurable (meetings booked, revenue generated)

You’ll have real ownership from day one

You’ll be early in building a new category (AI sales agents)

You’ll grow as fast as we do

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.

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

• 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.

We are seeking a Senior Full Stack Engineer to join our team in a long-term contractor capacity to continue development of a production-grade platform hosted on AWS.

This application supports policy processing, third-party integrations, compliance workflows, reporting, and intelligent automation capabilities. The ideal candidate is a strong software engineer first, capable of contributing across the full stack while helping scale and evolve the platform.

This role requires someone who can step into an existing system, understand complex workflows quickly, and independently deliver high-quality solutions.

Responsibilities

• Design, develop, and maintain full-stack application features across frontend and backend systems

• Build and support integrations with third-party systems and APIs

• Develop workflow-driven processes using Temporal

• Build scalable APIs and backend services using Python

• Maintain and optimize relational databases using PostgreSQL

• Develop reporting and analytics capabilities using charting libraries

• Contribute to AI-enabled features and integrations within the platform

• Improve CI/CD pipelines and deployment processes

• Participate in architecture discussions and help shape long-term technical direction

• Work closely with business and technical stakeholders to deliver production-ready solutions

Required Qualifications

Engineering

• at least 7+ years of full stack software engineering experience

• Strong proficiency in Python

• Strong frontend development experience with modern web frameworks

• Strong backend API development experience

• Experience designing and building scalable applications

• Strong understanding of software architecture and best practices

• Experience working in complex, integrated systems

Workflow and Orchestration

• Proven experience with Temporal

• Experience building and managing workflow orchestration patterns

• Familiarity with asynchronous processing and event-driven systems

Database and Reporting

• Strong experience with PostgreSQL

• Strong SQL and data modeling experience

• Experience building reporting dashboards and analytics features

• Experience with charting libraries such as Chart.js, D3.js, or Plotly

DevOps

• Experience with CI/CD pipelines

• Familiarity with containerized deployments

• Experience with cloud environments and modern development workflows

Preferred Qualifications

• Experience in insurance, surplus lines, or compliance-based applications

• Experience integrating with third-party vendors and external APIs

• Experience with AI tooling, LLM integrations, and context engineering

• Experience building intelligent automation features

What We Are Looking For

• Self-driven and highly autonomous

• Strong problem-solving ability

• Comfortable with ownership and accountability

• Able to contribute with minimal supervision

• Strong communication skills in English

• Comfortable working U.S.-based business hours

Ideal Candidate

A senior full stack engineer who can quickly contribute to an active production system, own features end-to-end, and help expand a platform that sits at the center of complex business workflows and integrations. Send resume with projects and contact information.

The Role

We’re looking for a Senior Applied AI & Data Engineer to become our first dedicated AI and data engineer.

You’ll build conversational AI experiences across web, mobile, and in-store channels while developing the data foundation behind them. You’ll make key technical decisions and own your work through to production.

What You’ll Do

• Build AI assistants using tool calling to work with real product, search, and order systems

• Design guardrails and evaluation sets to ensure AI responses are accurate and safe

• Build real-time and voice-enabled AI experiences

• Improve product data quality through AI-assisted enrichment and review workflows

• Build data pipelines, analytics, and personalisation systems

• Work closely with web and mobile developers and help guide technical implementation

What You’ll Need

• 6+ years of experience building and running production backend systems

• Strong Python skills, plus experience with JavaScript/TypeScript backends

• Experience shipping at least one LLM-powered feature to real users

• Experience with search and relevance

• Experience building data pipelines and analytics stores

• Comfortable deploying and monitoring services on a major cloud platform

• Strong communication skills and the ability to work independently

Nice to Have

• Experience with speech or voice AI

• E-commerce or retail technology experience

• Experience building multilingual products

Job Title: Full Stack AI Engineer

Location: Remote/Hyderabad

Experience Level: 3-5

Salary Range: 12-18LPA

Application Link:https://beyond.ciltriq.com/apply/BUILD

Description:

Join a team building AI-powered systems that solve complex business problems and automate operational workflows across document processing, voice agents, enterprise integrations, workflow automation, and multi-agent systems.

Strong full-stack foundations: frontend state management, asynchronous user experiences and performance; backend API design, authentication, data modelling, databases, queues and distributed systems.

Strong coding ability in Python and JavaScript or TypeScript, with practical experience in modern frontend frameworks and backend services.

Requirements:

- Design and build complete systems: frontend applications, backend services, APIs, databases, data pipelines and integrations with customer systems.

- Build multi-agent workflows with clear agent responsibilities, tool access, shared state, context management, routing, handoffs and coordination across sequential and parallel tasks.

- Make agent execution dependable through durable state, checkpoints, retries, timeouts, idempotency, recovery and human approval or review where needed.

- Deliver document-processing pipelines, voice agents and retrieval-based AI applications, connecting model outputs to useful actions in real business workflows.

- Own quality in production: automated tests, AI evaluations, guardrails, observability, access controls, deployments, incident response and clear documentation.

- Choose where AI adds value and where deterministic software is the better fit. Balance accuracy, latency, cost, security and maintainability.

- Improve reusable engineering foundations, review code and help other engineers grow as the team expands.

- A solid understanding of tool calling, structured outputs, retrieval, context and memory management, model selection and evaluation.

- Practical cloud and deployment experience, including containers, CI/CD, secrets management, logging, monitoring and production debugging.

- Ability to reason from first principles, investigate failures across system boundaries and communicate technical decisions clearly to customers and teammates.

- Useful additional experience: Document AI and OCR, real-time voice systems, enterprise integrations, agent protocols such as MCP, and orchestration frameworks.

- Useful additional experience: Mentoring engineers or building reusable platforms.