python developer

Generative AI Engineer 

Role Overview:

You will be responsible for the hands-on development, coding, and deployment of AI-powered features. Your focus is on writing clean, efficient code to integrate LLMs into our existing tech stack, building robust data pipelines for RAG, and ensuring the reliability of model outputs through rigorous testing and optimization.

Key Responsibilities

• Application Implementation: Code and integrate LLM APIs (OpenAI, Anthropic, etc.) or local models into backend services using Python, FastAPI, etc.,
• MCP Server Development: Design and implement custom MCP servers using the official SDKs (Python/TypeScript) to expose internal databases, APIs, and file systems to AI agents.
• RAG Implementation: Build and maintain the "plumbing" for Retrieval-Augmented Generation—specifically coding the data ingestion scripts, text chunking logic, and metadata filtering.
• Vector DB Management: Perform day-to-day operations on vector databases (Pinecone, Milvus, etc.), including indexing, querying, and optimizing search retrieval.
• Prompt Programming: Develop, version-control, and refine complex prompt templates (using Jinja2 or similar) to ensure consistent structured outputs (JSON/YAML).
• Agent Development: Implement multi-step workflows using LangChain, LangGraph, CrewAI etc.,, focusing on tool-calling logic and error handling.
• Evaluation & Testing: Build automated test suites to detect "hallucinations" and measure accuracy using frameworks.
• Performance Tuning: Implement caching layers and streaming responses to reduce latency and improve the end-user experience; Token optimization.
• Data Pre-processing: Clean and tokenize datasets for model fine-tuning or high-quality context retrieval.

Technical Skills (The "Execution" Stack)

• Language: Advanced Python (Asyncio, Pydantic) and optional TypeScript/Node.js (for full-stack integration).
• AI Frameworks: Hands-on experience with any of LangChain, LlamaIndex, and Hugging Face Transformers. RAG and Vector search concepts.
• Data Handling: Proficiency in SQL and handling unstructured data formats (PDFs, Markdown, JSON).
• Deployment: Practical experience with Docker, GitHub Actions (CI/CD), and experience with OpenTelemetry, LangSmith, Weights & Biases etc., Understanding of evaluation/guardrails.
• MCP/API Proficiency: Deep understanding of RESTful APIs, Streaming HTTP, MCP server vs client, JSONRPC

Position Overview

We are seeking a highly skilled and experienced Senior AI/ML Developer to lead the development and integration of advanced AI solutions within our product ecosystem. This role involves close collaboration with cross-functional teams including product managers, data scientists, and engineers to build AI models that solve real-world integration challenges. The ideal candidate will have a strong foundation in machine learning, deep learning, and software development, along with hands-on experience deploying AI models in production environments.

Bachelor’s degree in computer science, Data Science, Mathematics, Engineering, or a related field.

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8+ years of experience in designing and implementing AI/ML solutions.

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Demonstrated ability to integrate AI models into production software.

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Excellent analytical thinking, communication, and problem-solving abilities.

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Ability to work autonomously as well as in a collaborative team setup.

Skills Required

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Dataset Development: Strong track record of building datasets for training and/or evaluating machine learning models.

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LLM and NLP Experience: Hands-on experience working with LLMs, RAG architecture, Natural Language Processing (NLP), or applying Machine Learning to solve real-world problems.

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Experience with LLM fine-tuning, prompt engineering, vector databases (e.g., Pinecone, FAISS) is highly desirable.

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Test Harness Automation for LLM Agents

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Familiarity with agent frameworks (e.g., Semantic Kernel, AutoGen, Lang Chain, etc.).

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Proficiency in Python and libraries like Pandas, NumPy, Scikit-learn, etc. containerization (Docker), and API frameworks (Flask, Fast API).

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Integration Knowledge: API development, data transformation, system integration

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Soft Skills: Communication, teamwork, adaptability, critical thinking

2–4 years of experience in backend development with Python or Golang.

● Solid understanding of RESTful APIs, microservices, and distributed systems.

● Strong knowledge of data structures, algorithms, and OOPS principles.

● Hands-on experience with relational and/or NoSQL databases.

● Familiarity with Linux development, Docker, and basic cloud concepts

(AWS/GCP/Azure).

● Proficiency with Git and version control workflows.

● Familiarity with AI-powered development tools or exposure to projects involving large

language models (LLMs) is a plus.

Strong analytical and debugging skills with the ability to solve complex problems.

Job Description:

We are looking for a hands-on AI Engineer with experience in Generative AI and Agentic AI to build and deploy production-ready AI solutions.

Key Responsibilities:

• Develop and deploy GenAI and Agentic AI applications.
• Build RAG pipelines, LLM workflows, and AI agents.
• Develop solutions using Python, LangChain, LangGraph, LlamaIndex, or similar frameworks.
• Implement tool calling, context retrieval, and LLM orchestration.
• Integrate AI solutions with APIs and cloud platforms.
• Work with AWS/Azure/GCP, Docker, and CI/CD.

Required Skills:

• Strong Python programming skills.
• 3+ years of GenAI/Agentic AI experience.
• RAG and LLM orchestration.
• LangChain / LangGraph / LlamaIndex / AutoGen / CrewAI / Semantic Kernel.
• MCP and A2A knowledge.
• Cloud, APIs, Docker, and CI/CD experience.

Preferred Experience:

Hands-on experience building and deploying production-ready AI solutions.

Skill Set

Large language,Artificial Intelligence,Machine Learning

- 4–7 years of experience in software engineering/AI roles

- Strong programming skills in Python or TypeScript (Java/Go is a plus)

- Hands-on experience with LLMs, RAG pipelines, and AI frameworks

- Experience building APIs and working with distributed systems

- Familiarity with Kubernetes, Docker, and CI/CD pipelines

- Experience with cloud platforms (AWS/Azure/GCP)

Excellent communication

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 a San Francisco-based AI infrastructure company working with leading frontier AI labs to build post-training data and evaluation infrastructure for foundation models. We are hiring a Python Developer to create high-quality datasets, reinforcement learning environments, and benchmarking pipelines used to improve and evaluate state-of-the-art LLMs. This is a remote role with flexible working hours.

Responsibilities

* Create and curate datasets for LLM post-training (SFT, RLHF, RL, preference optimization).

* Build and maintain RL environments for agent evaluation.

* Develop Python tooling for dataset generation, validation, and transformation.

* Evaluate models on custom benchmarks and testing pipelines.

* Collaborate with research and engineering teams to deliver client-specific post-training datasets.

* Work with terminal-first development workflows and cloud infrastructure.

Required Skills

* Strong Python programming skills.

* Understanding of LLM fundamentals and post-training concepts (SFT, RLHF, RL).

* Experience working with structured data (JSON, CSV, YAML).

* Git, Linux/Unix command line, and solid software engineering fundamentals.

Good to Have

Experience with RAG, agentic AI systems, Hugging Face Transformers, LoRA/PEFT, LangChain or LlamaIndex, vector databases (FAISS, Qdrant, Milvus, Pinecone, Weaviate, ChromaDB), Docker, AWS/GCP, FastAPI/Flask, Bash, CLI tooling, model evaluation frameworks, benchmarking, and AI infrastructure.

Compensation

Base Salary: USD $1,250/month

Equity: ESOP/Equity package included.

Performance Bonuses: Up to USD $4,000/month (in addition to base salary).

Location

Remote (Worldwide)

Work Hours

Flexible, remote-first, asynchronous work environment.

How to Apply

Apply here: https://tally.so/r/wLReJG

Please complete the application form and submit the required details. Only shortlisted candidates will be contacted.

We are looking for a Python Backend Developer to design, build, and maintain scalable backend services and APIs. The role involves working with modern Python frameworks, databases (SQL and NoSQL), and building well-tested, production-grade systems.

You will collaborate closely with frontend developers, AI/ML engineers, and system architects to deliver reliable and high-performance backend solutions.

Key Responsibilities

• Design, develop, and maintain backend services using Python
• Build and maintain RESTful APIs using FastAPI
• Design efficient data models and queries using MongoDB and SQL databases (PostgreSQL/MySQL)
• Ensure high performance, security, and scalability of backend systems
• Write unit tests, integration tests, and API tests to ensure code reliability
• Debug, troubleshoot, and resolve production issues
• Follow clean code practices, documentation, and version control workflows
• Participate in code reviews and contribute to technical discussions
• Work closely with cross-functional teams to translate requirements into technical solutions

Required Skills & Qualifications

Technical Skills

• Strong proficiency in Python
• Hands-on experience with FastAPI
• Experience with MongoDB (schema design, indexing, aggregation)
• Solid understanding of SQL databases and relational data modelling
• Experience writing and maintaining automated tests
• Unit testing (e.g., pytest)
• API testing
• Understanding of REST API design principles
• Familiarity with Git and collaborative development workflows

Good to Have

• Experience with async programming in Python (async/await)
• Knowledge of ORMs/ODMs (SQLAlchemy, Tortoise, Motor, etc.)
• Basic understanding of authentication & authorisation (JWT, OAuth)
• Exposure to Docker / containerised environments
• Experience working in Agile/Scrum teams

What We Value

• Strong problem-solving and debugging skills
• Attention to detail and commitment to quality
• Ability to write testable, maintainable, and well-documented code
• Ownership mindset and willingness to learn
• Teamwork

What We Offer

• Opportunity to work on real-world, production systems
• Technically challenging problems and ownership of components
• Collaborative engineering culture

Position: Python Developer – Flask

Experience: 3+ Years

Location: Bangalore

Joining: Immediate Joiner Preferred

Employment Type: Full-Time

About the Role

We are looking for a skilled and passionate Python Developer with strong experience in Flask framework to join our growing development team. The ideal candidate should have hands-on experience in developing scalable backend applications, REST APIs, and integrating databases/services in a fast-paced environment.

Key Responsibilities

Develop, test, and maintain backend applications using Python and Flask.

Design and build RESTful APIs for web and mobile applications.

Collaborate with frontend developers, QA teams, and product stakeholders.

Write reusable, scalable, and efficient code.

Integrate third-party APIs and services.

Optimize application performance and troubleshoot issues.

Work with databases and ensure data security and integrity.

Participate in code reviews and technical discussions.

Maintain proper documentation for development activities.

Required Skills

3+ years of experience in Python development.

Strong hands-on experience with Flask framework.

Good understanding of REST API development.

Experience with MySQL/PostgreSQL/MongoDB databases.

Familiarity with ORM libraries such as SQLAlchemy.

Knowledge of Git/version control systems.

Understanding of software development lifecycle and agile methodologies.

Basic understanding of Docker and deployment processes is an added advantage.

Good debugging and problem-solving skills.

Preferred Skills

Experience with AWS/Azure or cloud platforms.

Exposure to microservices architecture.

Familiarity with CI/CD pipelines.

Knowledge of frontend technologies like React or Angular is a plus.

Educational Qualification

Bachelor’s degree in Computer Science, Information Technology, or related field.

What We’re Looking For

Immediate joiner or candidates with short notice period.

Strong communication and teamwork skills.

Self-driven and proactive mindset

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)