The Role
Build and deploy production LLM and machine learning solutions, including RAG pipelines, agentic workflows, document intelligence, classical ML models, evaluation harnesses, guardrails, APIs, and cloud deployments. The role emphasizes measurable quality, cost control, retrieval optimization, MLOps, model selection, and client-facing technical solutioning. Candidates need strong Python, LLM application development, vector database, ML, evaluation, API, Docker, cloud, and Git experience, with 4–7 years in ML or software engineering and at least two years building generative AI applications.
Summary Generated by Built In
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 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
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
Skills Required
- 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 machine learning or software engineering
- At least 2 years building LLM or generative AI applications
- Strong production-grade Python, including clean typed code, testing, async patterns, and dependency/environment management
- Hands-on experience building applications with LLM APIs, including prompt design, tool calling, structured outputs, streaming, and token/cost management
- End-to-end experience building at least one retrieval-augmented generation system using chunking, embeddings, vector stores, and retrieval tuning
- Solid machine learning fundamentals and hands-on experience with scikit-learn and PyTorch or TensorFlow
- Demonstrated practice measuring AI output quality with test sets and metrics
- Ability to expose models as REST APIs, containerize with Docker, deploy to a cloud platform, and use Git
- At least one AI solution taken from prototype to live deployment with real users
- Experience with LangChain, LangGraph, LlamaIndex, agent frameworks, or Model Context Protocol
- OCR and document parsing experience using Azure Document Intelligence, AWS Textract, Unstructured, or similar tools
- Experience with AWS Bedrock, SageMaker, Azure AI Foundry, or Google Vertex AI
- Experience with LLM tracing and evaluation tools such as LangSmith, Langfuse, Arize, or Weights & Biases
- SQL, pandas, and reliable batch data pipeline development experience
- Voice AI, fine-tuning and serving open-weight models, knowledge graphs, computer vision, multimodal models, or text-to-SQL experience
- Awareness of GDPR, HIPAA, India's DPDP Act, and related AI data protection requirements
Am I A Good Fit?
Get Personalized Job Insights.
Our AI-powered fit analysis compares your resume with a job listing so you know if your skills & experience align.
Success! Refresh the page to see how your skills align with this role.
The Company
What We Do
Flatworld Solutions is a global IT, business consulting, and outsourcing firm founded in 2004. As a diversified BPO and technology services provider, it supports over 18,000 clients across 100 countries. The company leverages automation and Generative AI to help enterprises streamline operations, enhance efficiency, and achieve transformative growth through a wide range of professional services and IT solutions.
