AI/ML Engineer

Zohorecruit; Bengaluru; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Flatworld Solutions Pvt Ltd. in Bengaluru, India, seeks an AI/ML engineer to design and deploy LLM-powered components across RAG pipelines, document intelligence, summarisation, and conversational agents.

You will orchestrate multi-step workflows, tool calling, and guarded outputs while selecting models across major APIs to optimize quality, latency, and cost. Responsibilities include building ingestion and vector search pipelines, training ML models when appropriate, and exposing services as

Qualifications

• Proficiency in Python and production-grade code.
• Hands-on with LLM APIs (Anthropic, OpenAI, Google) and tool calling.
• Experience with retrieval-augmented systems and vector stores.
• Strong ML background with scikit-learn and PyTorch/TensorFlow.

Responsibilities

• Design and build LLM-powered components and RAG pipelines.
• Develop agent workflows with tool calling and multi-step orchestration.
• Engineer prompts and structured outputs; version and test like code.
• Manage model selection across multiple APIs to balance quality and cost.
• Build data ingestion, embeddings, and metadata enrichment pipelines.
• Create evaluation harnesses and monitor AI quality and cost.

Skills

Python

LLM development

RAG pipelines

Vector databases

ML fundamentals

Cost management

Education

Bachelor's or Master's in CS/AI/DS

Tools

FastAPI

Docker

Git

PyTorch / TensorFlow

Pinecone / Qdrant / Chroma

LangChain

Job description

Flatworld Solutions Pvt Ltd. | Full time

Bangalore North, India | Posted on 10/05/2026

Design and build LLM-powered components - RAG pipelines, document intelligence, summarisation, classification, extraction, and conversational agents - across multiple solution concepts in parallel.

Develop agentic workflows using tool calling, multi-step orchestration, and clear guardrails and fallback behaviour.

Engineer prompts, system instructions, and structured output schemas; version and test them like code.

Select the right model for each task across commercial APIs (Anthropic, OpenAI, Google, Azure OpenAI, AWS Bedrock) and open-weight models, balancing quality, latency, and cost.

Build ingestion pipelines for client data: document parsing (PDFs, scans, spreadsheets), chunking strategies, embedding generation, and metadata enrichment.

Design and tune retrieval - vector search, hybrid (keyword + semantic) search, re-ranking, and query rewriting.

Build, train, and evaluate classical ML models (classification, forecasting, anomaly detection) where the problem calls for them rather than an LLM.

Assess client data readiness during discovery and flag quality, volume, or privacy gaps early.

Fine-tune or adapt models (e.g. LoRA) only when prompting and retrieval are not enough, backed by a

C. Evaluation, Quality & Cost Control

Build evaluation harnesses for every AI component: golden datasets, automated metrics, LLM-as-judge scoring, and human review loops.

Measure and reduce hallucinations, retrieval misses, and edge-case failures before anything goes in front of a client.

Track token usage, latency, and cost per transaction; provide running-cost inputs for solution pricing and

Implement guardrails: PII redaction, prompt injection defences, content filtering, and output validation.

Package AI services as clean APIs (FastAPI or equivalent) that the Full Stack Engineer can integrate without friction.

Containerise and deploy AI services to cloud platforms (AWS, Azure, GCP); monitor quality drift, latency, and cost in live environments.

Support the AI Solutions Lead in pre-sales - assess technical feasibility, answer model and data questions, and contribute architecture notes to proposals.

Maintain a reusable library of retrieval modules, evaluation scripts, prompt templates, and agent patterns so each new engagement starts further along.

Document model choices, evaluation results, and known limitations for every build.

Requirements

Mandatory Technical Requirements

The following are non-negotiable for this role:

• Python: Strong production-grade Python - clean, typed, tested code; async patterns; dependency and environment management. [MANDATORY]
• LLM Application Development: Hands-on experience building on LLM APIs (Anthropic, OpenAI, Google, or Azure OpenAI) - prompt design, tool/function calling, structured outputs, streaming, and token and cost management. [MANDATORY]
• RAG & Vector Databases: At least one retrieval-augmented system built end to end - chunking, embeddings, vector stores (Pinecone, Qdrant, Chroma, pgvector, or similar), and retrieval tuning. [MANDATORY]
• ML Fundamentals: Solid grounding in supervised learning, evaluation metrics, overfitting, and embeddings, with hands-on use of scikit-learn and PyTorch or TensorFlow. [MANDATORY]
• AI Evaluation: Demonstrated practice of measuring AI output quality with test sets and metrics - not just manual spot-checks. [MANDATORY]
• API Development & Deployment: Ability to expose models as REST APIs, containerise with Docker, and deploy to a cloud platform; proficiency with Git. [MANDATORY]

Strongly Preferred

• Orchestration Frameworks: LangChain, LangGraph, LlamaIndex, or equivalent; experience with agent frameworks and the Model Context Protocol (MCP).
• Document AI: OCR and document parsing (Azure Document Intelligence, AWS Textract, Unstructured, or similar) for messy enterprise documents.
• Cloud AI Platforms: AWS Bedrock / SageMaker, Azure AI Foundry, or Google Vertex AI.
• Observability: LLM tracing and evaluation tools such as LangSmith, Langfuse, Arize, or Weights & Biases.
• Data Engineering: SQL, pandas, and building reliable batch data pipelines.

Advantageous

Not required, but a clear differentiator for this role:

• Voice AI experience - speech-to-text, text-to-speech, and real-time voice agent pipelines with telephony integration.
• Fine-tuning and serving open-weight models (Llama, Mistral, Qwen) with vLLM, TGI, or Ollama.
• Knowledge graphs, graph-based retrieval, or text-to-SQL systems over enterprise data.
• Computer vision or multimodal model experience.
• Awareness of data protection requirements (GDPR, HIPAA, India's DPDP Act) and how they shape AI

What We Look For (Beyond the Stack)

• Evidence over enthusiasm - you trust an evaluation score more than a good-looking demo.
• Pragmatism in model choice: you reach for the simplest approach that works, whether that is a prompt, a classifier, or a rule.
• Cost awareness - you think about what a solution costs to run at 10,000 requests a day, not just whether it works once.
• Ability to explain AI behaviour, limits, and risks in plain language to non-technical colleagues and clients.
• A GitHub profile, Kaggle record, published work, or side projects that show what you build when nobody assigns it.

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, Statistics, Engineering, or equivalent practical experience.
• 4 - 7 years of hands-on experience in ML or software engineering, including at least 2 years building LLM or Generative AI applications.
• At least one AI solution taken from prototype to live deployment with real users.
• Prior experience in an AI product company, an IT services AI practice, a startup, or an innovation lab is a

What We Offer

• Variety - you will build across multiple industries and AI use cases rather than tuning one model forever.
• Direct line of sight from your models to a real client decision.
• Access to current commercial and open-weight models, with freedom to pick the right tool for each problem.
• Mentorship from the AI Solutions Lead and exposure to enterprise solutioning and pre-sales.
• Learning budget for AI/ML upskilling, conferences, and cloud certifications.
• Competitive compensation with a clear path toward Senior AI Engineer or AI Solution Architect tracks