Freelanacers/Translators

Shape the future of Generative AI by designing intelligent prompts and RAG systems for enterprise applications.

What you'll do:

• Engineer advanced prompts for GPT-4o, Claude 3.5, Llama 3
• Build Retrieval-Augmented Generation (RAG) pipelines
• Fine-tune open-source LLMs on domain-specific datasets
• Create AI agents with tool calling and memory
• A/B test prompts for accuracy, bias, and response quality
• Develop chatbots for e-commerce, healthcare, legal use cases

What we need:

• Python proficiency, basic ML concepts
• Experience with ChatGPT/Claude APIs
• Strong logical reasoning and writing skills
• Curiosity about LLMs (no PhD required!)

Outcomes:

• Published AI research papers (co-authored)
• Live chatbot deployments in portfolio
• Interview-ready for Prompt Engineer roles

About Ziro.digital: Ziro.digital is an AI career intelligence platform built for engineering students — helping them navigate internships, placements, and early-career decisions with AI-powered tools.

What you'll do:

• Build and test LLM-powered features for our web product: resume analysis, mock interviews, and personalised career recommendations.

• Work with Python and LLM APIs (Claude, OpenAI, Gemini), applying prompt engineering and building RAG pipelines.

• Write automation scripts and integrate AI capabilities directly into the Ziro.digital web product.

• Ship features end-to-end, working closely with the founder.

What we're looking for:

• Pursuing or completed B.Tech / M.Tech / MCA (2025–2026 pass-outs preferred).

• Strong Python fundamentals.

• Hands-on LLM / ML / AI-agent projects — please share your GitHub.

• Familiarity with Generative AI, Prompt Engineering and RAG is a strong plus.

• Willing to work full-time, in-office at our KPHB, Hyderabad location.

• Able to join immediately for a 6-month internship.

The role:

• Title: AI Intern

• Openings: 2

• Duration: 6 months (full-time, in-office at KPHB, Hyderabad)

• Stipend: ₹10,000–15,000/month

• Performance-based PPO: ₹2–5 LPA, based on impact

• Experience: 0–1 years

• Must-haves: Python, LLMs, Generative AI, Prompt Engineering, RAG

How to apply: Share your resume and GitHub portfolio. We'll shortlist quickly and get you started.

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

AI Product Builder | Consumer Experiences

TL;DR: ₹1 lakh/month. HSR Layout, Bengaluru. Full-time, in-person preferred, with flexibility for an exceptional fit. Use AI to build and refine consumer experiences. No CV or prior experience required. Apply by email, then an unpaid 48-hour workout assignment.

I'm Vishnu, building Aira. AI is the biggest equaliser we have, but nobody has figured out the consumer use case yet. I think Aira will be the reason everyday people start using AI daily.

I don't believe chatting is the final interface for AI. So Aira is built around a hand-drawn character that feels alive, shapes what's on your screen, and brings voice, animation and music together. The goal is the best answer, delivered in the best way for your situation, with help acting on it.

Say you're deciding what to cook. Aira helps you pick a meal, shows the ingredients, arranges the purchase with your confirmation. While you cook, it keeps the recipe and your current step on screen, speaks short instructions, and waits while your hands are busy. The same screen won't work at the gym, in meditation or while fixing a tyre. The conversation, the actions and what you see and hear should feel like one experience that adapts to what you're doing.

I'm hiring people who would have wanted to work on the first iPhone. People who obsessed over the small details that made something unfamiliar feel natural, and cared about what a person felt the first time they used it.

We've built the foundation. Your job is to take an experience, decide how it should work, build it with AI, and keep agonising until it feels right. In practice: tuning prompts and behaviour files, building or changing screen components with AI, and testing what you've made.

You may not yet be able to explain why an interaction feels incomplete. I want you to follow that feeling and build a better version. That's why we don't ask for a CV, prior experience or a coding background. We need product thinking, creativity and ownership — you question what AI gives you, make it work, and keep improving it.

We're launching in San Francisco this month. You'll work directly with me and help shape what people experience the first time they meet Aira.

The process

You'll build a workout experience from a minimal starter. We give you a stepper, a timer and the component plumbing; you decide how it behaves and build the rest. Anyone can attempt it, before any screening. You have 48 hours after receiving it. It's unpaid, for evaluation only, and your work stays yours.

AI helps me review, but I pick the shortlist myself. Shortlisted candidates get a 30-minute conversation. I aim to reply within 24 hours of submission and finish within a week.

Support with design and build to prove out agentic AI solution flow by working with other data 

scientists and engineers to build, train Large Language Model (LLM) architectures, RAG 

systems, and autonomous agentic workflows 

Key qualifications: 

>> AI solution design & Development: Design Agentic AI solutions using RAG (Retrieval-

Augmented Generation) and orchestration frameworks like LangGraph or LangChain. 

>> Model Fine-Tuning: Solid understanding and experience with Pre-train, fine-tune, and 

optimize open-source like BERT, LLama, and other proprietary foundation models for domain-

specific tasks 

>> Solid Stats and ML foundations and (vibe) coding skills with Python, PySpark 

>>  Implement validation frameworks and tracing practices (using tools like Arize) to monitor 

agent behavior, guard against model drift, and ensure compliance 

>> Collaborate with Engineering to deploy models securely on cloud and on-prem ecosystems 

Role Overview

We are looking for an experienced professional to evaluate AI-generated responses for conversational AI agents used in travel-related products. The role focuses on assessing response accuracy, relevance, completeness, and quality to improve AI performance.

Key Responsibilities

• Evaluate AI-generated responses against predefined guidelines and quality standards.
• Perform data annotation, labeling, classification, and quality reviews.
• Identify incorrect, misleading, irrelevant, or incomplete responses and potential AI hallucinations.
• Analyze evaluation data to identify recurring errors, quality gaps, and improvement opportunities.
• Track quality metrics, document findings, and recommend improvements.
• Use Excel/Google Sheets and basic SQL, where applicable, to analyze evaluation data.
• Collaborate with AI and Machine Learning teams to improve conversational AI performance.

Required Skills

• 5–8 years of experience in Data Annotation, AI Evaluation, Content Quality, Search Relevance, or Quality Assurance.
• Strong analytical, problem-solving, and attention-to-detail skills.
• Experience applying evaluation guidelines and quality standards consistently.
• Proficiency in Excel or Google Sheets.
• Good written communication and documentation skills.
• Understanding of quality metrics and data analysis.

Good to Have

• Exposure to Generative AI, LLMs, conversational AI, or AI response evaluation.
• Experience with Human-in-the-Loop (HITL), AI training data validation, or hallucination detection.
• Basic SQL knowledge.
• Experience in travel, hospitality, or search quality evaluation.

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)

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

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.