AI Research Intern (NLP/LLM Focus)

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 PGAGI:

We're at the forefront of creating advanced AI systems, from fully autonomous agents that provide intelligent customer interaction to data analysis tools that offer insightful business solutions. We are seeking enthusiastic interns who are passionate about AI and ready to tackle real-world problems using the latest technologies.

About the Role

We are at the forefront of building advanced AI systems — from fully autonomous agents that power intelligent customer interactions, to data analysis tools that deliver actionable business insights. This internship combines hands-on AI/ML engineering with robust backend development, giving you a complete picture of how production-grade AI systems are built, deployed, and scaled.

You will work across the full stack: designing and fine-tuning AI models on one end, and architecting the APIs, databases, and server-side infrastructure that bring those models to life on the other.

Key Responsibilities

AI / ML ENGINEERING

Design, experiment with, and fine-tune large language models (LLMs) and NLP pipelines for real-world use cases.

Develop and iterate on prompt engineering strategies to optimise model performance and output quality.

Integrate and deploy models using Hugging Face and OpenAI platforms; evaluate open-source alternatives.

Build deep learning workflows including training, evaluation, and continuous improvement loops.

Collaborate on data collection, preprocessing, and feature engineering for ML pipelines.

BACKEND ENGINEERING

Architect and develop scalable RESTful APIs and GraphQL endpoints using Node.js / FastAPI / Express.

Build and maintain full-stack features using Next.js (App Router), integrating server-side rendering, API routes, and React components.

Design and manage relational and NoSQL databases (PostgreSQL, MongoDB, or equivalent); write efficient queries and manage schema migrations.

Implement authentication, authorisation, and security best practices (JWT, OAuth 2.0, role-based access control).

Containerise services with Docker and contribute to CI/CD pipelines for reliable, automated deployments.

Integrate AI/ML model inference endpoints into backend services, handling async processing, queuing, and latency optimisation.

Write clean, well-tested, and well-documented backend code; participate actively in code reviews.

VERSION CONTROL & COLLABORATION

Use Git and GitHub for all version control workflows — branching strategies, pull requests, and code reviews.

Contribute to technical documentation, architecture decision records, and internal knowledge bases.

Duration & Compensation

Duration: 6 Months

Stipend: Base ₹8,000/month — up to ₹15,000/month based on performance

Post-Internship: Full-time opportunity as AI/ML Engineer (₹6–8 LPA) based on performance

Perks & Benefits

Hands-on experience shipping real AI products used by actual customers.

Mentorship from senior engineers and industry experts in AI/ML and backend development.

Exposure to the full development lifecycle — from model training to production deployment.

Collaborative, innovative, and flexible work environment.

Accelerated growth path with a clear route to a full-time engineering role.

How to Apply

Interested candidates are invited to submit their resume and complete the assignment using

the link : https://pgagi.in/jobs/28df1e98-f0c3-4d58-9509-d5b1a4ea9754

Shortlisted candidates will be contacted for an interview.

Selection Process

Initial Screening: We'll review your application for evidence of your skills, experience, and a strong foundation in AI.

Task Assignment: Candidates need to submit assignment which is already being attached in careers page , designed to assess your practical skills.

Performance Review: Our experts will evaluate your task submission, with excellence in this stage being crucial for further consideration.

Interview: Impressive task performers will be invited for an interview to discuss their potential contribution to our team.

Onboarding: Successful candidates will join our team, with exciting projects ahead

Requirements

MUST-HAVE SKILLS

Strong proficiency in Python for AI/ML development and scripting.

Solid understanding of JavaScript / TypeScript; experience with Node.js and Next.js (or a strong willingness to learn quickly).

Familiarity with REST API design principles and at least one backend framework (Express, FastAPI, Django, etc.).

Working knowledge of at least one database system (PostgreSQL, MySQL, MongoDB).

Experience with Git and GitHub version control workflows.

Exposure to AI/ML platforms such as Hugging Face and OpenAI.

Understanding of prompt engineering concepts and the model fine-tuning process.

GOOD TO HAVE

Hands-on experience with Next.js App Router, React Server Components, or similar modern full-stack frameworks.

Familiarity with Docker, basic DevOps concepts, or cloud platforms (AWS, GCP, or Azure).

Experience with message queues (Redis, RabbitMQ) or background task processing.

Knowledge of LLM orchestration tools such as LangChain or LlamaIndex.

SOFT SKILLS & MINDSET

Strong problem-solving instincts and a genuine curiosity about AI technology.

Ability to own tasks end-to-end and communicate progress clearly.

Comfortable working in a fast-moving environment where requirements evolve.

A growth mindset — eager to learn, receive feedback, and level up continuously.

TECH YOU'LL WORK WITH

Python

Next.js

Node.js

FastAPI

LLMs / NLP

PostgreSQL

React

Docker

HuggingFace

OpenAI API

GitHub

REST / GraphQL

Apply now to embark on a transformative career journey with PGAGI, where innovation and talent converge!

#artificialintelligence #Machinelearning #AI #AIML #LLM #FastAPI #NLP #openAI #AImodels #AIMLInternship #AIintern #Internship #aimlgraduate #Python

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.

Job Title: AI Architecture Intern

Company: PGAGI Consultancy Pvt. Ltd.

Location: Remote

Employment Type: Internship

Position Overview

We're at the forefront of creating advanced AI systems, from fully autonomous agents that provide intelligent customer interaction to data analysis tools that offer insightful business solutions. We are seeking enthusiastic interns who are passionate about AI and ready to tackle real-world problems using the latest technologies.

Duration: 6 months

Key Responsibilities:

• AI System Architecture Design: Collaborate with the technical team to design robust, scalable, and high-performance AI system architectures aligned with client requirements.
• Client-Focused Solutions: Analyze and interpret client needs to ensure architectural solutions meet expectations while introducing innovation and efficiency.
• Methodology Development: Assist in the formulation and implementation of best practices, methodologies, and frameworks for sustainable AI system development.
• Technology Stack Selection: Support the evaluation and selection of appropriate tools, technologies, and frameworks tailored to project objectives and future scalability.
• Team Collaboration & Learning: Work alongside experienced AI professionals, contributing to projects while enhancing your knowledge through hands-on involvement.

Requirements:

• Strong understanding of AI concepts, machine learning algorithms, and data structures.
• Familiarity with AI development frameworks (e.g., TensorFlow, PyTorch, Keras).
• Proficiency in programming languages such as Python, Java, or C++.
• Demonstrated interest in system architecture, design thinking, and scalable solutions.
• Up-to-date knowledge of AI trends, tools, and technologies.
• Ability to work independently and collaboratively in a remote team environment

Perks:

- Hands-on experience with real AI projects.

- Mentoring from industry experts.

- A collaborative, innovative and flexible work environment

Compensation:

- Stipend: Base is INR 8000/- & can increase up to 20000/- depending upon performance matrix.

After completion of the internship period, there is a chance to get a full-time opportunity as an AI/ML engineer.

Preferred Experience:

• Prior experience in roles such as AI Solution Architect, ML Architect, Data Science Architect, or AI/ML intern.
• Exposure to AI-driven startups or fast-paced technology environments.
• Proven ability to operate in dynamic roles requiring agility, adaptability, and initiative.

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.

7

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.

9

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

10

Mandatory (Age) - Candidate's Age should be below 28 Years

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.

LLM/AI Model Engineer – Training, Evaluation & Benchmarking

Location: New Delhi, India (On-site)

Employment Type: Full-time

Experience: 3–5 years

Industry: Enterprise AI / Generative AI / Machine Learning / NLP

About QubeLabs

QubeLabs is building the next generation of Enterprise AI Systems that transform workforce operations and intelligence for the financial services industry.

Our platform combines Conversational AI, Agentic AI, workflow automation, proprietary language models and enterprise intelligence. Our flagship product, QubeLabs Workmate, serves banks, NBFCs, MFIs, wealth management firms, insurance companies and fintechs across India and Europe.

At the core of our AI platform is the Vectro Series, our proprietary family of language models designed for enterprise and financial-services use cases.

We are looking for an LLM/AI Model Engineer to build the training pipelines, datasets and evaluation infrastructure required to continuously improve the Vectro Series.

Role Overview

You will work closely with the Head of AI, Research Partners and engineering teams to implement and operationalize LLM training approaches and build a reliable model improvement lifecycle:

Data Preparation → Training → Evaluation → Benchmarking → Error Analysis → Model Improvement

The role combines hands-on LLM fine-tuning, dataset engineering, tokenization, evaluation framework development and model quality management.

Key Responsibilities

1. LLM Training & Fine-tuning

Build and maintain pipelines for training and fine-tuning open-source foundation models for the Vectro Series.

Implement supervised fine-tuning (SFT), parameter-efficient fine-tuning (PEFT), LoRA/QLoRA and domain adaptation techniques; support continued pre-training where required.

Manage training configurations, checkpoints, model versions and experiment tracking.

Optimize training workflows for computational efficiency, reproducibility and reliability.

Translate model development approaches defined with the Head of AI and Research Partners into practical, scalable training pipelines.

2. Training Data & Tokenization

Build data pipelines to collect, clean, filter, normalize, deduplicate and validate training data.

Develop instruction-tuning, SFT and domain-specific datasets for financial-services and enterprise use cases.

Implement tokenization workflows, tokenizer configuration, vocabulary management, sequence packing, truncation and padding.

Address data representation challenges involving financial terminology, numerical values and multilingual content.

Maintain dataset versioning, lineage and quality controls, incorporating identified data gaps and relevant model failures into future training data.

3. Golden Dataset & Model Evaluation

Build and maintain QubeLabs' Golden Dataset to evaluate key model capabilities and target use cases.

Develop automated evaluation pipelines and reusable evaluation harnesses, combining automated metrics with human assessment where appropriate.

Evaluate accuracy, reasoning, factuality, instruction following, safety, multilingual performance and financial-domain capabilities.

Establish consistent evaluation methodology and regression tests to measure changes across model versions.

4. Benchmarking & Error Analysis

Evaluate Vectro against relevant public benchmarks and proprietary QubeLabs BFSI/enterprise benchmarks.

Compare results with relevant open-source and commercial models using consistent evaluation conditions.

Analyze failures, identify root causes across data, training and model behavior, and translate findings into actionable improvements.

Maintain reproducible benchmark results and reports to track model strengths, limitations and progress.

5. Model Quality & Release

Define model release quality gates and ensure each major release meets agreed evaluation and benchmark criteria.

Support red-teaming, robustness and safety testing.

Coordinate with the Head of AI, Research Partners and engineering teams to ensure validated model improvements are suitable for integration into production systems.

Experience

3–5 years in Machine Learning, Deep Learning, NLP, Generative AI or related fields.

Hands-on experience training or fine-tuning LLMs using open-source foundation models.

Practical experience building training pipelines, preparing datasets and evaluating model performance.

Experience with GPU-based training; distributed training is an advantage.

Financial-services, multilingual AI or enterprise AI experience is preferred.

Required Skills

LLM & Machine Learning: Large Language Models, Transformer architectures, Generative AI, NLP, Deep Learning, SFT, PEFT, LoRA/QLoRA, fine-tuning and continued pre-training.

Frameworks & Infrastructure: Python, PyTorch, Hugging Face Transformers, Hugging Face Tokenizers, GPU computing, experiment tracking and ML pipelines.

Data & Tokenization: Tokenization, tokenizer configuration, text preprocessing, sequence packing, dataset construction, data quality, versioning and lineage.

Evaluation & Benchmarking: Golden datasets, LLM evaluation, evaluation harnesses, automated and human evaluation, LLM-as-a-Judge, public and domain-specific benchmarks, regression testing and error analysis.

The role requires the ability to build both the model training pipeline and the measurement system that determines whether the model has actually improved.

What Success Looks Like

Reliable and reproducible training and fine-tuning pipelines for the Vectro Series.

High-quality training datasets, tokenization workflows and a comprehensive Golden Dataset.

Automated evaluation and benchmarking infrastructure with measurable model quality gates.

Clear, reproducible evidence of model performance against public, proprietary BFSI and relevant competing-model benchmarks.

A continuous improvement loop that converts evaluation findings into measurable gains across successive Vectro releases.

Key Performance Indicators

Training pipeline reliability and experiment throughput.

Training data quality and coverage.

Golden Dataset and evaluation coverage.

Benchmark reproducibility and model performance improvement.

Accuracy of error diagnosis and effectiveness of regression detection.

Time required to complete training, evaluation and benchmarking cycles.

Compliance with model release quality criteria.

Why Join QubeLabs?

Build the proprietary Vectro Series of enterprise and financial-services language models.

Develop model training, evaluation and benchmarking infrastructure from the ground up.

Work closely with the Head of AI and Research Partners.

Solve real-world AI challenges across financial services.

Develop proprietary datasets, benchmarks and model improvement systems.

Contribute directly to production AI systems serving customers across India and Europe.

Preferred Candidate Profile

B.Tech, M.Tech, MS or PhD in Computer Science, AI, ML, Mathematics or a related field.

3–5 years of relevant experience with strong practical skills in Python, PyTorch, LLM fine-tuning and training data pipelines.

Working knowledge of tokenization, evaluation datasets, benchmarking and regression testing.

Experience with open-source models such as Llama, Qwen, Mistral or similar is preferred.

Ability to independently build reliable model training and evaluation systems from scratch.

QubeLabs is an equal opportunity employer and values diversity and inclusion. We encourage applications from qualified candidates regardless of background or identity.

About Us

Corporate Web Solutions provides technology and digital transformation solutions with an emphasis on emerging technologies including artificial intelligence and machine learning.

Role Overview

As an AI & Machine Learning Intern, you'll gain practical experience working with datasets, machine learning models, AI concepts, and Python-based tools.

Key Responsibilities

• Prepare and analyze datasets.
• Assist in developing machine learning models.
• Experiment with AI and ML algorithms.
• Evaluate model performance.
• Work with Python and relevant libraries.
• Participate in practical AI/ML projects.

Requirements

• Basic Python programming knowledge.
• Understanding of machine learning fundamentals.
• Basic knowledge of data handling.
• Familiarity with NumPy and Pandas is a plus.
• Eagerness to learn AI technologies.

Perks

• Certificate of Internship
• Flexible work hours
• Mentorship and real project exposure
• Potential for PPO
• Letter of Recommendation
• Performance-Based Stipend available up to ₹18,000/month

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