Senior GenAI Engineer — RAG (Full-Stack)

Primary Roles & Responsibilities

▸ At SDE-2 you will build, and maintain end-to-end RAG pipelines — from ingestion and chunking to retrieval and LLM response synthesis 

▸ Design and develop MCP (Model Context Protocol) servers that power AI agents and multi-step workflows 

▸ Development on Next.js / React.js frontend modules for our AI product suite 

▸ Own PostgreSQL database design — schema modelling, indexing strategy, query optimisation, and migration management 

▸ Integrate third-party AI APIs (Gemini, OpenAI, Anthropic, etc.) and open-source models into production-grade applications  

▸ Work directly with Founders for product and leadership to scope features, estimate effort, and make architectural recommendations 

▸ Stay ahead of the AI engineering curve — evaluate new tools, frameworks, and models and bring recommendations to the team 

▸ Deep hands-on experience with RAG: multi-stage retrieval, re-ranking, hybrid search, prompt engineering 

▸ Experience building or extending MCP servers — understanding of tool calling, context passing, and agent orchestration 

▸ Strong proficiency in Next.js and React.js — you've built production UIs with proper state management, routing, and performance considerations 

▸ PostgreSQL expertise: advanced queries, indexing, schema design for complex domains, migrations in production 

▸ TypeScript proficiency — you write it by default, not as an afterthought 

▸ Experience with at least one vector database (pgvector, Pinecone, Weaviate, Qdrant) 

Good to Have: 

▸ Experience with streaming LLM responses, tool/function calling, and multi-agent frameworks 

▸ Familiarity with LangChain, LlamaIndex, or custom agentic implementations 

▸ Exposure to cloud infrastructure (AWS / GCP / Azure) and containerisation (Docker) 

▸ Knowledge of frontend design systems, Tailwind CSS.

▸ Prior experience working directly with product or business stakeholders 

Hiring AI Engineer

Exp : 5 - 8 yrs

Edu : BE/B.Tech/MCA

Work Location : Pune WFO

Skills :

Min 4 years strong programming experience in Python is a MUST.

Min 2 years hands-on experience in AI with LLMs, RAG pipelines, and AI frameworks

RAG with deep retrieval experience (most important):

Owned a RAG system end to end in production, not a demo.

Has built an MCP server or client, or built agents using LangGraph/Semantic Kernel (tool calling, multi-step workflows).

Strong AI Engineer / Machine Learning Engineer profiles.

2

Mandatory (Experience 1) – Must have minimum 3+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.

3

Mandatory (Experience 2) – Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.

4

Mandatory (Experience 3) – Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.

5

Mandatory (Experience 4) – Must have hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.

6

Mandatory (Experience 5) – Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.

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Mandatory (Experience 6) – Must have hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.

8

Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.

Mandatory ( Pedigree) - B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are Considered.

We are seeking a senior Generative AI Systems Expert with 8+ years of deep engineering experience to architect production-grade AI systems (RAG pipelines, fine-tuning, prompt optimization, inference scaling). This is a specialized Individual Contributor (IC) role for a production-focused builder who owns the AI implementation end-to-end—from initial architecture to deployment, optimization, and evaluation.

While your primary mastery lies in building and scaling robust LLM applications, you possess a strong, rigorous engineering mindset. Experience or strong familiarity with Quality Assurance (QA), Test Automation, and AI evaluation frameworks is highly preferred to ensure our systems behave reliably and deterministically at scale. You are not just implementing off-the-shelf models; you are designing complex retrieval systems, optimizing inference pipelines, evaluating model trade-offs, and building systems that reliably serve millions of transactions.

🎯 What We’re Looking For (Who You Are)

• AI Systems Architect (Core): 4+ years of dedicated experience building and shipping LLM systems in production. You understand model selection, RAG architecture, fine-tuning strategies, inference optimization, and cost management. You make pragmatic trade-offs between accuracy, latency, and cost.
• Production-Focused Builder: You've shipped LLM systems at scale. You are deeply comfortable with tokenization, context windows, vector database optimization, handling hallucinations, and system failures.
• Independent Operator: You own AI features end-to-end—from problem definition through evaluation, deployment, and production monitoring. You unblock yourself and make good decisions with incomplete information.
• Quality & Evaluation-Minded (Highly Valued Plus): You possess a rigorous engineering background with a strong appreciation for testing. You understand how to handle non-deterministic outputs through deterministic test design, evaluate model performance (F1, precision, recall), and build/use automation frameworks to prevent regressions.
• Team Amplifier: You mentor engineers on AI best practices, participate in code reviews, and help scale GenAI adoption across the organization.
• Continuous Experimenter: You A/B test approaches, iterate based on production data, and stay current with the rapid evolution of LLMs.

🛠️ Technical Stack & Qualifications

GenAI Core & Architecture (Must-Have)

• LLM Ecosystem: Deep experience with LLM APIs (OpenAI, Anthropic, Hugging Face), model trade-offs, and selection.
• Retrieval: RAG pipeline architecture and vector databases (Pinecone, Weaviate, FAISS).
• Optimization: Prompt engineering, fine-tuning (PEFT/LoRA), and inference optimization.
• Operations: Cost/latency management, hallucination detection, and mitigation.
• Advanced Patterns: Agentic patterns, multi-step reasoning, and building for reliability under uncertainty.
• Education: Degree in Computer Science, Mathematics, or equivalent professional experience. A practical portfolio demonstrating GenAI/ML implementation is valued highly.

Testing & Automation (Highly Desirable / Good-to-Have)

• Domain Expertise: 5+ years in the QA/Testing domain.
• AI-Specific Validation: Regression testing for models, hallucination testing, bias detection, and mocking LLM APIs.
• Deterministic Design: Handling non-deterministic AI outputs through deterministic test frameworks.
• Automation Suite: Proficiency in test case design (positive, negative, edge cases) and frameworks like pytest, Jest, Selenium, or Cypress.
• CI/CD: Experience integrating automated test execution into CI/CD pipelines.

💼 Key Responsibilities

• Build: Design and implement end-to-end GenAI systems—RAG pipelines, fine-tuning strategies, and prompt optimization. Make pragmatic choices between model options, latency/cost trade-offs, and production constraints.
• Ship & Guard: Own the deployment of AI features. Architect the system to ensure reliability, scalability, and proper handling of edge cases (hallucinations, rate limits, failures) using robust validation strategies.
• Measure & Evaluate: Instrument systems with telemetry. Run A/B experiments comparing AI approaches. Establish metrics dashboards to track both system performance and model accuracy/alignment over time.
• Collaborate: Mentor engineers on AI best practices. Participate in code reviews. Help the team adopt and scale GenAI capabilities safely and effectively.

Position: Senior/Lead Full Stack Engineer – Gen AI / Agentic AI

Experience: 7+ Years

Employment: Permanent Position

Location: Banglore / Hyderabad

Job Summary

We are looking for a Senior/Lead Full Stack Engineer – Gen AI / Agentic AI with strong hands-on experience in Python, React.js, MongoDB, Java/Spring Boot and Generative AI/Agentic AI.

The candidate should have experience designing and developing scalable enterprise applications and implementing production-grade LLM, RAG, AI Agent and multi-agent solutions.

Key Responsibilities

• Design, develop and maintain scalable full-stack applications using Python, React.js, MongoDB and Java/Spring Boot.
• Build production-grade Generative AI and Agentic AI applications using LLMs and modern AI frameworks.
• Develop RAG pipelines, AI agents, tool calling, memory management, planning and agent orchestration.
• Work with LangChain, LangGraph, MCP, vector databases and semantic search.
• Develop Python-based APIs, microservices and asynchronous applications using FastAPI/Flask/Django.
• Build REST APIs and event-driven microservices with focus on scalability, performance and resilience.
• Integrate LLMs, embeddings, vector stores and external enterprise tools/services.
• Implement prompt engineering, LLM evaluation, guardrails and AI observability.
• Develop responsive front-end applications using React.js.
• Work with MongoDB, SQL and hybrid data models.
• Implement CI/CD pipelines and support cloud/OCP deployments.
• Follow secure coding, testing, code quality and performance best practices.
• Participate in architecture, technical design, code reviews and mentoring of team members.
• Collaborate with business and technical stakeholders to translate requirements into scalable solutions.

Mandatory Skills

1. Python
2. React.js
3. Gen AI / Agentic AI
4. RAG + LLM
5. LangChain / LangGraph
6. MongoDB
7. Java + Spring Boot
8. REST APIs / Microservices
9. Vector Databases / Embeddings
10. MCP / AI Agent orchestration

Good to Have

• FastAPI / Flask / Django
• Kafka / Solace
• Docker / Kubernetes
• AWS / Azure / GCP / OCP
• CI/CD – Jenkins / GitHub Actions
• LLMOps / AI evaluation / observability
• ELK / Grafana / Splunk / AppDynamics
• SQL / NoSQL
• Agile/Scrum

Dear Candidate,

We have an immediate opening with Mphasis for Senior Full Stack Engineer (Python AI).

📍 Location: Hyderabad / Bangalore

💼 Experience: 5+ Years

🏢 Employment: Permanent

⚡ Immediate Joiners Preferred

Mandatory Skills:

• Python
• Java + Spring Boot
• React.js
• MongoDB

GenAI

Role: Full Stack GEN AI Engineer

Location: Remote - Bengaluru

Duration: Fulltime With VDart Digital

The role demands a developer who is not just familiar with Large Language Models (LLMs), but is an expert in building autonomous agentic workflows using the modern GenAI stack (LangChain, CrewAI, Vector DBs). Expertise in system design, cloud-native technologies, and CI/CD for AI-driven applications is essential for this high-impact delivery role.

Responsibilities

· Design, develop, and maintain full-stack applications that are scalable, robust, and meet the company's quality standards, with a specific focus on Generative AI integration.

· Agentic Orchestration: Build and deploy sophisticated multi-agent systems and autonomous workflows using frameworks like LangChain, CrewAI, or LangGraph.

· Collaborate effectively with cross-functional teams to define, design, and ship new features that bridge the gap between raw AI power and intuitive user workflows.

· Exhibit strong problem-solving skills with an emphasis on product development and driving architecture choices that enable a world-class user experience.

· Utilize a variety of modern web technologies and frameworks (React.js, Angular, or Vue.js) to build responsive and accessible user interfaces.

· Develop and maintain RESTful APIs and services with optimal performance and scalability, handling streaming AI responses and complex function-calling logic.

· Ensure code quality, organization, and automatization through best practices, including unit tests for prompts, model evaluation pipelines, and automated CI/CD for AI-driven features.

· Implement and optimize RAG (Retrieval-Augmented Generation) pipelines using Vector Databases and advanced retrieval techniques.

· Adapt to emerging technologies and frameworks, specifically new GenAI tools and frontier models, and apply them to operational and business needs.

· Manage individual project priorities, deadlines, and deliverables with minimal supervision.

Skill Requirements

· Excellent oral and written communication skills, with the ability to articulate complex AI and technical ideas to both technical and non-technical audiences.

· Profound knowledge of application development, data structures, networking, operating systems, and DBMS.

· Strong proficiency in backend programming languages for API development such as Python, Java, JavaScript (Node.js), or Go.

· Expertise in front-end technologies and frameworks such as React.js, Angular, or Vue.js.

· GenAI & Agentic Tools: Deep hands-on experience with LangChain, CrewAI, or AutoGen. Ability to manage agent memory, state, and tool-calling.

· In-depth understanding of SQL/NoSQL databases, data modeling, and experience with Vector Databases for RAG implementations.

· Solid grasp of system design, microservices architecture, and cloud-native technologies including Docker, Kubernetes, and GitHub Actions.

· Experience with distributed computing, machine learning frameworks, and tools, with a primary focus on Generative AI.

· Desirable (Good to Have): Experience with Generative or Adaptive UI development, where the interface dynamically adapts or renders components based on LLM outputs and real-time AI reasoning.

· A strong desire to learn and master new technologies and techniques in the rapidly evolving GenAI landscape.

Qualifications

· Bachelor’s degree in Computer Science, Engineering, or a related field.

· A minimum of 3-5 years of experience in full-stack development, with a proven track record of building and deploying Generative AI applications and agents.

· Portfolio of successfully deployed web applications and services, specifically showcasing AI agents, RAG implementations, or complex AI-driven features.

We are seeking Generative AI Developers with strong Python programming and AI/ML expertise to build, deploy, and optimize LLM-powered applications. The role involves developing RAG solutions, AI agents, and enterprise GenAI applications while collaborating with cross-functional teams.

Key Responsibilities

• Develop and enhance Generative AI applications using LLMs and AI frameworks.
• Build and optimize RAG pipelines, vector search, and AI-powered workflows.
• Design effective prompts and fine-tune models using techniques such as LoRA and QLoRA.
• Develop REST APIs and integrate AI capabilities into enterprise applications.
• Deploy, monitor, and maintain AI solutions in cloud and containerized environments.
• Ensure code quality through testing, debugging, documentation, and code reviews.
• Follow Responsible AI, security, and data governance practices.

Required Technical Skills

• Strong proficiency in Python, OOP, APIs, debugging, and software development best practices.
• Good understanding of Data Structures & Algorithms, complexity analysis, and problem-solving.
• Hands-on experience with LLMs, Prompt Engineering, RAG, AI Agents, and embeddings.
• Experience with LangChain, LangGraph, LlamaIndex, Hugging Face, or similar frameworks.
• Knowledge of vector databases, semantic/hybrid search, and retrieval architectures.
• Experience with PyTorch, TensorFlow, or Keras.
• Familiarity with Docker, Git, CI/CD, and cloud platforms (Azure/AWS/GCP).
• Understanding of AI governance, data privacy, and Responsible AI principles.

Preferred Skills

• Experience with Agentic AI frameworks (CrewAI, AutoGen, Semantic Kernel).
• Exposure to Azure AI Foundry, Databricks, or enterprise AI platforms.
• Knowledge of multimodal AI applications.

Qualifications

• Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related field.
• 5 years of software development experience, including AI/ML or Generative AI projects.
• Experience building and deploying production-grade AI solutions.

Assessment Focus Areas

Candidates will be evaluated on:

• Python coding and problem-solving
• Data Structures & Algorithms
• LLMs, RAG, and Agentic AI concepts
• API development and system design
• Cloud deployment and AI solution architecture

Role Overview

We are looking for a Python Developer with strong experience in Generative AI and LLM-based applications. The candidate should have hands-on experience building AI solutions using Python, RAG, LangChain/LangGraph, and related GenAI technologies.

Mandatory Skills

Python, GenAI/LLM, RAG, LangChain/LangGraph, Agentic AI, FastAPI, REST API, Vector Database, Prompt Engineering, Microservices

Key Responsibilities

• Develop and maintain applications using Python and modern frameworks.
• Build GenAI/LLM-based applications and solutions.
• Develop RAG pipelines using vector databases.
• Work with LangChain/LangGraph for LLM and agent-based applications.
• Develop and integrate REST APIs using FastAPI.
• Implement Agentic AI workflows and AI-powered features.
• Integrate LLMs with existing applications and microservices.
• Apply prompt engineering techniques to improve AI application performance.

Immediate Joiner - 15 days Notice period

• Primary Skillsets: Python, Generative AI, Agentic AI, LLM, experience with designing and developing production grade AI solutions, AWS/Azure/GCP, LLMOps, Data Engineering (Databricks/Snowflake), Regulated GenAI, Data and Decisions, and Agentic Systems.

• Preferred: Pharma or life sciences experience, especially in regulated (GxP) environments, experience setting up a team or practice from scratch.