AI Prompt Engineering Intern

Job Description – Python & Generative AI Engineer

Python & Generative AI Engineer

Location: Bangalore, India

Experience: 4+ Years

Employment Type: Full-time

Job Summary

We are looking for an experienced Python & Generative AI Engineer with 4+ years of software development experience and strong hands-on expertise in Python, LLMs, Generative AI, and AI application development.

The ideal candidate should be comfortable building production-grade AI solutions, integrating LLMs with enterprise applications, and developing scalable APIs and services using Python.

Key Responsibilities

• Design, develop, and deploy Generative AI applications using Python and modern AI/ML frameworks.
• Work with Large Language Models (LLMs) such as GPT, Claude, Gemini, Llama, or similar models.
• Develop RAG (Retrieval-Augmented Generation) pipelines using vector databases and embedding models.
• Build AI-powered applications using frameworks such as LangChain, LangGraph, LlamaIndex, or equivalent.
• Develop scalable REST APIs and backend services using FastAPI, Flask, or Django.
• Integrate LLM APIs, prompt engineering, function/tool calling, and structured outputs into enterprise applications.
• Work with vector databases such as FAISS, Pinecone, Weaviate, Milvus, or Azure AI Search.
• Implement document processing, chunking, embeddings, semantic search, and knowledge retrieval solutions.
• Evaluate LLM responses for accuracy, relevance, hallucination, latency, and cost.
• Build and maintain production-ready AI pipelines with appropriate monitoring, logging, security, and error handling.
• Collaborate with data scientists, ML engineers, software engineers, and business stakeholders to deliver AI solutions.
• Write clean, reusable, testable, and well-documented Python code.
• Participate in architecture discussions, code reviews, testing, deployment, and production support.

Required Skills

• 4+ years of experience in software/Python development.
• Strong proficiency in Python and object-oriented programming.
• Hands-on experience with Generative AI and LLM-based applications.
• Experience with OpenAI/Azure OpenAI, Anthropic, Google Gemini, or open-source LLMs.
• Strong understanding of Prompt Engineering and LLM application patterns.
• Experience implementing RAG pipelines.
• Knowledge of embeddings, vector databases, semantic search, and document retrieval.
• Experience with LangChain, LangGraph, LlamaIndex, or similar frameworks.
• Strong experience developing REST APIs using FastAPI/Flask/Django.
• Familiarity with Git, CI/CD, Docker, and cloud platforms such as AWS, Azure, or GCP.
• Good understanding of SQL and databases.
• Strong problem-solving and communication skills.

Good to Have

• Experience with AI agents / Agentic AI and tool/function calling.
• Experience with multi-agent frameworks.
• Knowledge of MLOps/LLMOps and model evaluation.
• Experience with Kubernetes and containerized deployments.
• Knowledge of NLP, machine learning, or deep learning.
• Experience with Azure AI, AWS Bedrock, Amazon SageMaker, or Google Vertex AI.
• Understanding of responsible AI, data privacy, and LLM security.

Education

Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related field.

🚀 Hiring: Python GenAI / Agentic AI Engineer

📍 Location: Hyderabad

💼 Experience: 7+ Years

🤖 GenAI / Agentic AI: 2+ Years

Mandatory Skills:

• Strong Python development experience

• Generative AI / Agentic AI

• LLMs & Prompt Engineering

• LangChain / LangGraph

• RAG & Vector Databases

• AI Agents / Multi-Agent Systems

• FastAPI / REST APIs

• LLM Integration

• Microservices & API Architecture

• Git & CI/CD

Looking for candidates with strong hands-on experience in building GenAI/Agentic AI solutions using Python, LLMs, RAG, and AI Agent frameworks.

About the role

We are seeking an AI Engineer to build and implement AI systems for content production at scale. You'll work at the intersection of engineering and content designing prompt pipelines, integrating generative models, and building the tooling that turns source material into finished creative output. The ideal candidate is technically strong but also has taste: someone who understands story and craft, and can tell the difference between output that's technically correct and output that's actually good.

Responsibilities

• Build and iterate on prompt pipelines and multi-agent workflow components
• Design and integrate agentic workflows orchestrate multi-step, tool-using agents that plan, call models, and hand off between stages in production
• Deploy and serve open-source models set up inference endpoints, manage GPU compute, and optimize for latency and cost
• Write evals compare outputs against references, quantify quality, and feed results back into the pipeline
• Work on data pipelines: structured extraction from messy source text, localization, similarity/dedup
• Debug and maintain pipeline stages in production

What you bring:  

• (1+/3+) years of engineering experience, or a strong portfolio of shipped projects
• Solid Python fundamentals clean, working, readable code
• Hands-on experience with LLM APIs and prompt engineering (personal projects count)
• Comfort with Git, REST APIs, and working in a Linux environment
• A feel for content and narrative you can judge whether generated output is actually good, not just valid
• Curiosity and clear communication you ask good questions and don't stay stuck silently

 Preferred

• Exposure to agent/orchestration frameworks (LangGraph, LangChain, CrewAI)
• Familiarity with vector databases, embeddings, or RAG (Qdrant, pgvector)
• Hands-on work with open-source generative media models Flux, LTX, Wan, or similar
• Experience deploying open-source models for inference (vLLM, ComfyUI, Replicate/Cog, Docker + GPU)
• Experience writing evals or LLM-as-judge scoring
• Node.js and Fastapi familiarity, or experience deploying on AWS

About the role

We are seeking an AI Engineer to build and implement AI systems for content production at scale. You'll work at the intersection of engineering and content designing prompt pipelines, integrating generative models, and building the tooling that turns source material into finished creative output. The ideal candidate is technically strong but also has taste: someone who understands story and craft, and can tell the difference between output that's technically correct and output that's actually good.

Responsibilities

• Build and iterate on prompt pipelines and multi-agent workflow components
• Design and integrate agentic workflows orchestrate multi-step, tool-using agents that plan, call models, and hand off between stages in production
• Deploy and serve open-source models set up inference endpoints, manage GPU compute, and optimize for latency and cost
• Write evals compare outputs against references, quantify quality, and feed results back into the pipeline
• Work on data pipelines: structured extraction from messy source text, localization, similarity/dedup
• Debug and maintain pipeline stages in production

What you bring:  

• (1+/3+) years of engineering experience, or a strong portfolio of shipped projects
• Solid Python fundamentals clean, working, readable code
• Hands-on experience with LLM APIs and prompt engineering (personal projects count)
• Comfort with Git, REST APIs, and working in a Linux environment
• A feel for content and narrative you can judge whether generated output is actually good, not just valid
• Curiosity and clear communication you ask good questions and don't stay stuck silently

 Preferred

• Exposure to agent/orchestration frameworks (LangGraph, LangChain, CrewAI)
• Familiarity with vector databases, embeddings, or RAG (Qdrant, pgvector)
• Hands-on work with open-source generative media models Flux, LTX, Wan, or similar
• Experience deploying open-source models for inference (vLLM, ComfyUI, Replicate/Cog, Docker + GPU)
• Experience writing evals or LLM-as-judge scoring
• Node.js and Fastapi familiarity, or experience deploying on AWS

Roles & Responsibilities

• Design and develop intelligent AI-based applications using advanced NLP and LLM techniques to solve real-world business challenges in financial services.
• Build and optimize Retrieval-Augmented Generation (RAG) pipelines leveraging structured and unstructured financial data.
• Integrate and orchestrate LLMs/SLMs for question-answering, summarization, semantic search, and document understanding.
• Develop and maintain RESTful APIs (sync and async) to serve NLP models and chatbot interfaces using frameworks like FastAPI, Flask, etc.
• Should have knowledge of advanced prompting techniques.
• Implement semantic search, hybrid search, and text retrieval systems using Elasticsearch and vector databases (e.g., FAISS, Pinecone, Weaviate).
• Perform NLP tasks such as entity recognition, text classification, intent detection, embedding generation, and sentiment analysis where required.
• Monitor and fine-tune LLM/SLM performance with real-world user data to improve relevance, latency, and accuracy.
• Exposure to LLMOps tools for monitoring, evaluation, and versioning of AI models in production.
• Build, train, and evaluate deep learning models for NLP tasks including classification, NER, summarization, and embedding generation.
• Develop traditional machine learning models (e.g., regression, decision trees, clustering) for structured data analysis and prediction tasks.
• Interact with cross-functional teams to understand system issues and follow up with respective teams to get them fixed.
• Understand and identify areas of improvement across businesses and participate in solution identification and implementation.
• Should be able to work as an Individual Contributor on new and existing projects.
• Positive and problem-solving attitude, must work as an independent contributor.

Ideal Candidate

1.Strong Data Scientist / AI Engineer / Generative AI Engineer profile.

2.Mandatory (Experience 1) - Must have 3+ years of hands-on experience in Data Science, Artificial Intelligence, Machine Learning, Deep Learning, NLP, or Generative AI application development.

3.Mandatory (Experience 2) - Must have strong hands-on experience in Python programming, backend development, API development, and production-grade application support

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

5.Mandatory (Experience 4) - Must have hands-on experience in NLP use cases such as text classification, sentiment analysis, entity recognition (NER), semantic search, embeddings, or document understanding.

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

.7.Mandatory (Experience 6) - Must have hands-on experience building or implementing Retrieval Augmented Generation (RAG) solutions, vector search, semantic search, or knowledge-based AI applications.

8.Mandatory (Experience 7) - Must have experience with Prompt Engineering and Generative AI frameworks such as LangChain, LangGraph, AI Agents, Azure OpenAI, or similar technologies.

9.Mandatory (Experience 8) - Must have experience developing, consuming, or integrating APIs using Python frameworks such as FastAPI, Flask, or similar technologies.

10.Mandatory (CTC) - The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.

11.Preferred (Experience 1) - Experience with LLMOps/MLOps tools for monitoring, evaluation, experimentation, and versioning of AI models.

12.Preferred (Experience 2) - Exposure to Azure OpenAI, Azure Kubernetes Service (AKS), Kubernetes, cloud-native AI deployments, or distributed systems.

13.Preferred (Experience 3) - Experience working with PostgreSQL, MongoDB, Redis, Kafka, or large-scale data platforms.

14.Preferred (Experience 4) - Familiarity with Docker, Kubernetes, cloud platforms, and scalable deployment architecture.

15.Preferred (Company) - Candidates from AI-first startups, product companies, SaaS organizations, fintech, or data-driven technology companies.

16.Mandatory ( Age ) - Candidate Should be Below 28 Years.

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

KEY RESPONSIBILITIES:

•Build agents with persistent context & memory

•Design self-learning feedback loops

•Implement RAG pipelines for domain knowledge

•Manage conversation state & orchestration

•Integrate with LLM APIs (OpenAI, Claude, open-source)

Iterate fast — ship daily, measure weekly

MUST-HAVE SKILLS

•Python / TypeScript proficiency

•LangChain, CrewAI, AutoGen or custom frameworks

•Experience with vector DBs (Pinecone, Weaviate, Qdrant)

•Prompt engineering & evaluation pipelines

•Understanding of agent architectures (ReAct, tool-use)

Git, CI/CD, containerization basics

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

Strong Data Scientist / AI Engineer / Generative AI Engineer profile.

2

Mandatory (Experience 1) - Must have 3+ years of hands-on experience in Data Science, Artificial Intelligence, Machine Learning, Deep Learning, NLP, or Generative AI application development.

3

Mandatory (Experience 2) - Must have strong hands-on experience in Python programming, backend development, API development, and production-grade application support.

4

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

5

Mandatory (Experience 4) - Must have hands-on experience in NLP use cases such as text classification, sentiment analysis, entity recognition (NER), semantic search, embeddings, or document understanding.

6

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

7

Mandatory (Experience 6) - Must have hands-on experience building or implementing Retrieval Augmented Generation (RAG) solutions, vector search, semantic search, or knowledge-based AI applications.

8

Mandatory (Experience 7) - Must have experience with Prompt Engineering and Generative AI frameworks such as LangChain, LangGraph, AI Agents, Azure OpenAI, or similar technologies.

9

Mandatory (Experience 8) - Must have experience developing, consuming, or integrating APIs using Python frameworks such as FastAPI, Flask, or similar technologies.

10

Mandatory (CTC) - The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.

11

Preferred (Experience 1) - Experience with LLMOps/MLOps tools for monitoring, evaluation, experimentation, and versioning of AI models.

12

Preferred (Experience 2) - Exposure to Azure OpenAI, Azure Kubernetes Service (AKS), Kubernetes, cloud-native AI deployments, or distributed systems.

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.

Location: Hyderabad, India (home base), deployed at client sites in India. Occasional Middle East exposure possible.

About the Role

You will work as a senior AI engineer who embeds inside a customer's business. Your job is to learn how the business makes money, find the highest value problem, and build a working system that solves it.

Four behaviors define this role:

1. Go where the work happens. You work onsite with the customer, in the room where decisions are made.
2. Show working software early. You build a prototype in days, not a document in weeks.
3. One person owns the outcome. You are the single point of accountability for the result.
4. Stay after go-live. You keep running and improving the system after launch.

You are the single point of accountability. You are not a solo builder. A full KnackLabs engineering team in Hyderabad builds and runs the production systems behind you.

This role involves extended onsite deployments at client locations in other cities, sometimes up to six months at a stretch. Please apply only if you are ready for this way of working.

What you'll own

1. Discovery - Learn how the customer makes money. Find the highest value problem to solve first.
2. The prototype - Build a working prototype fast, using real or sample data, to prove the idea.
3. The roadmap - Decide what to build, in what order, and set clear success measures tied to business outcomes.
4. The build - Design and ship the production system with the Hyderabad engineering team. This includes data integration, agents, retrieval, and evaluations.
5. The client relationship - Be the trusted technical contact for the customer, from engineers to senior leaders.
6. Go live and after - Deploy the system, watch how it performs, fix problems, and improve it over time.
7. Feedback to the product - Share what you learn in the field so the vendor's product and our internal tools get better.

What we are looking for

1. Around 4 or more years of software engineering experience, including customer-facing or client delivery work.
2. Strong programming skills in Python. Working knowledge of TypeScript or JavaScript.
3. A full-stack development experience with strength in backend technologies.
4. Production experience with large language models, including prompt engineering and agent development.
5. You build with AI coding tools like Claude Code or Codex as your default way of working, and you have shipped real apps or agents this way.
6. Experience building retrieval-augmented generation (RAG) systems: chunking, embeddings, vector databases, retrieval, and reranking.
7. Experience building and deploying AI systems.
8. Experience integrating with APIs and enterprise systems.
9. Experience with at least one cloud platform (AWS, Azure, or GCP).
10. Clear communication. You can explain a technical choice to an engineer and to a business leader.
11. High ownership and comfort with ambiguity. You can take an unclear problem and turn it into a plan.
12. Willingness to work onsite at client locations in India for extended periods, and to travel as the work needs.

Nice to have

1. Experience with on-premises or private cloud (VPC) deployments.
2. Experience with observability and tracing tools such as LangSmith or Braintrust.
3. Experience with data engineering and pipelines.
4. A history of side projects, open source contributions, or products you shipped end-to-end.
5. Experience in embedded or forward-deployed roles before.
6. Experience working at a consulting or professional services firm in a client-facing delivery role.

Stack and tools

1. Languages: Python and TypeScript.
2. Models: Claude and other frontier or open-source models, chosen to fit the customer.
3. AI patterns: RAG, agents, prompt engineering, and evaluations.
4. Vector and retrieval: vector databases and retrieval pipelines.
5. Cloud: AWS, Azure, or GCP, on public or private cloud.
6. Integration: REST APIs and enterprise system connectors.