Full-Stack AI Engineer (Agentic AI & LLM Systems)

Company Description Frensei Innovation Labs builds AI-native communication intelligence infrastructure that converts unstructured communication data into actionable financial insights. The company’s flagship product, ClawbackVault, helps financial services organizations protect revenue by detecting pre-departure intent before it becomes financial loss. Frensei focuses on behavioural and linguistic signals within professional relationships to surface risk and opportunity in real time. The team is expanding its communication intelligence capabilities across sectors where relationship quality directly impacts economic outcomes. This environment offers engineering talent the chance to shape cutting-edge AI systems with direct, measurable business impact.
Job Title: Full-Stack AI Engineer – Agentic AI & Intelligent Applications
Department: Engineering / AI & Product Development
Employment Type: Full-time
Experience Level: Mid-Level to Senior (flexible based on demonstrated ability)
Location: Remote
Compensation Range: ₹35,000 to ₹70,000
About the Role
We are looking for a versatile Full-Stack AI Engineer who can design, develop, and deploy intelligent applications by combining modern frontend and backend engineering with AI engineering and agentic AI capabilities.
The ideal candidate will have experience building scalable web applications, integrating AI models and APIs, developing LLM-powered workflows, and implementing autonomous or semi-autonomous AI agents that solve real-world business problems.
You will work across the entire product lifecycle—from understanding business requirements and designing application architecture to building user interfaces, developing APIs, integrating AI capabilities, deploying services, and monitoring production performance.
We are particularly interested in engineers who understand how to move AI solutions beyond prototypes into reliable, secure, scalable, and production-ready products.
Key Responsibilities
1. Full-Stack Application Development
* Design, develop, and maintain responsive, high-performance web applications.
* Build modern frontend interfaces using React.js, Next.js, TypeScript, or equivalent technologies.
* Develop backend services, RESTful APIs, and asynchronous processing systems using Python, FastAPI, Node.js, or equivalent frameworks.
* Design and manage relational and NoSQL databases, including PostgreSQL, MongoDB, and Redis.
* Implement authentication, authorization, API integrations, and secure data-handling mechanisms.
* Develop reusable components, modular services, and maintainable application architectures.
2. AI Engineering and LLM Integration
* Build AI-powered applications using commercial and open-source large language models (LLMs).
* Integrate models and APIs from providers such as OpenAI, Anthropic, Google, and other relevant AI platforms.
* Develop prompt pipelines, structured outputs, tool-calling workflows, and context-aware AI applications.
* Implement Retrieval-Augmented Generation (RAG), semantic search, embeddings, and document-processing pipelines.
* Work with vector databases and search technologies such as pgvector, Pinecone, Qdrant, Weaviate, or equivalent systems.
* Optimize AI applications for latency, inference cost, accuracy, reliability, and user experience.
* Evaluate model outputs using automated tests, benchmarks, and task-specific evaluation frameworks.
3. Agentic AI and Multi-Agent Systems
* Design and develop AI agents capable of reasoning through tasks, selecting tools, and executing multi-step workflows.
* Build agentic applications using frameworks such as LangGraph, LangChain, LlamaIndex, or equivalent technologies.
* Implement tool calling, workflow orchestration, state management, memory, and context handling.
* Develop multi-agent systems where appropriate, with clear responsibilities, communication patterns, and execution controls.
* Integrate agents with external APIs, databases, internal services, enterprise tools, and business workflows.
* Implement human-in-the-loop approvals, permission boundaries, retries, fallback strategies, and safeguards for sensitive operations.
* Evaluate agent performance for task completion, correctness, execution cost, and failure recovery.
4. Production AI Systems and MLOps
* Deploy and operate AI-powered applications using cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
* Containerize applications using Docker and work with CI/CD pipelines and infrastructure automation.
* Implement logging, tracing, monitoring, error handling, and observability across application and AI workflows.
* Track model and agent performance, token consumption, latency, costs, and failure rates.
* Apply appropriate security practices, including secrets management, input validation, access controls, and protection against prompt injection.
* Contribute to automated testing, deployment pipelines, incident investigation, and production reliability.
* Understand when to use hosted APIs, open-source models, conventional software logic, or traditional machine learning.
5. Product Engineering and Collaboration
* Translate business requirements into technical designs and deliverable engineering tasks.
* Collaborate with product managers, designers, data scientists, and other engineers.
* Participate in architecture reviews, code reviews, technical documentation, and sprint planning.
* Identify opportunities to automate repetitive workflows and improve business decision-making through AI.
* Take ownership of features from initial design through implementation, deployment, and continuous improvement.
Required Technical Skills
Frontend
* Strong proficiency in JavaScript or TypeScript.
* Hands-on experience with React.js and preferably Next.js.
* Understanding of responsive design, component architecture, state management, and frontend API integration.
Backend
* Strong programming skills in Python and/or TypeScript/JavaScript.
* Experience with FastAPI, Django, Flask, Node.js, or comparable backend frameworks.
* Understanding of API design, asynchronous processing, authentication, caching, and event-driven architectures.
* Practical database experience with SQL and at least one NoSQL or vector-storage technology.
AI Engineering
* Practical experience integrating LLMs into software applications.
* Understanding of prompting, embeddings, context windows, structured generation, and tool calling.
* Hands-on exposure to RAG pipelines, vector search, and AI application evaluation.
* Familiarity with agentic AI frameworks and multi-step workflow orchestration.
* Ability to identify and mitigate hallucinations, retrieval failures, and unreliable agent execution.
Cloud and Software Engineering
* Experience with Git, testing, debugging, and collaborative development workflows.
* Familiarity with Docker, CI/CD, and at least one major cloud platform.
* Understanding of software security, scalable architecture, and production deployment practices.
Preferred Qualifications
* Experience building and deploying multi-agent or tool-using AI systems.
* Familiarity with MCP (Model Context Protocol) and standardized tool integrations.
* Experience with AI observability and evaluation tools such as LangSmith, Langfuse, or equivalent platforms.
* Knowledge of background job processing, queues, event streaming, and workflow orchestration.
* Exposure to Kubernetes, Terraform, serverless architectures, or infrastructure as code.
* Experience with AI guardrails, model routing, caching, and cost optimization.
* Familiarity with traditional machine learning, model serving, or fine-tuning where relevant.
* Experience delivering AI products to real users in a startup or enterprise environment.
What We Are Looking For
The ideal candidate is an engineer who:
* Thinks in terms of complete products, not isolated components.
* Can independently develop a frontend, backend, and AI integration.
* Understands the difference between a chatbot, an LLM workflow, and a genuinely tool-using agent.
* Knows when agentic autonomy is useful and when a deterministic workflow is more appropriate.
* Can turn experimental AI concepts into tested, observable, and maintainable production systems.
* Is comfortable working in a fast-moving environment where technologies and requirements evolve.
* Communicates technical trade-offs clearly and takes ownership of delivery.
Qualifications
* Bachelor’s or Master’s degree in Computer Science, Software Engineering, Artificial Intelligence, or a related discipline, or equivalent practical experience.
* Relevant professional experience building full-stack applications and/or AI-powered software.
* A strong portfolio of deployed applications, GitHub projects, or demonstrable technical work is highly valued.
We prioritize demonstrated engineering ability and practical project experience over academic qualifications alone.
Success in This Role
Within the first 90 days, the successful candidate should aim to:
* Understand the existing app

Work arrangement
Yes