Key Responsibilities

A. LLM & Generative AI Solution Build

- 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.

B. Data, Retrieval & Model Development

- 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 clear cost-benefit case.

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 client ROI models.
- Implement guardrails: PII redaction, prompt injection defences, content filtering, and output validation.

D. Deployment, MLOps & Collaboration
- 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 explicit 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 solution design.

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 plus.

Benefits

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

📌 AI/ML Engineer (Bengaluru)
🏢 Flatworld Solutions
📍 Bengaluru