Fractal Analytics: Gen AI Engineer – AWS

About Gen AI Engineer – AWS at Fractal Analytics

Fractal Analytics is a global leader in artificial intelligence and advanced analytics, powering human decisions across Fortune 500 enterprises through cutting-edge machine learning and generative artificial intelligence. As a Gen AI Engineer specializing in Amazon Web Services (AWS) at Fractal's innovation hub in Bengaluru, you will be at the vanguard of enterprise AI transformation. This role was created to design, build, and deploy production-grade Large Language Model (LLM) architectures, Retrieval-Augmented Generation (RAG) pipelines, and autonomous agent frameworks that solve high-impact business problems.

In your day-to-day engineering workflows, you will work closely with data scientists, cloud architects, and full-stack engineering pods to engineer robust Generative AI systems. You will build scalable microservices using Python and FastAPI, integrate foundation models through AWS Bedrock and Amazon SageMaker, and implement vector retrieval mechanisms using Pinecone, OpenSearch, or pgvector. Your responsibilities span model evaluation, prompt optimization, and cloud infrastructure orchestration, ensuring enterprise-grade scalability, low latency, and deterministic output quality across mission-critical client deployments.

Fractal Analytics provides ambitious early-career AI engineers an extraordinary environment characterized by rapid technological innovation, deep domain expertise, and high-visibility client impact. Over your first twelve months, you will advance from foundational model integration to architecting sophisticated multi-agent orchestration frameworks and fine-tuning custom models. For engineers dedicated to mastering generative AI engineering within an AI-first culture, Fractal provides an unmatched runway for accelerated technical leadership.

Roles & Responsibilities

• Design, build, and optimize scalable Generative AI applications and autonomous multi-agent workflows leveraging Python, LangChain, and LlamaIndex.
• Implement resilient Retrieval-Augmented Generation (RAG) pipelines utilizing advanced vector databases such as Amazon OpenSearch, Pinecone, or pgvector.
• Integrate and orchestrate foundational LLMs and multi-modal models utilizing AWS Bedrock, Amazon SageMaker, and Hugging Face model hubs.
• Develop high-throughput, low-latency RESTful APIs and asynchronous backend microservices using modern web frameworks such as FastAPI or Flask.
• Formulate, experiment with, and optimize structured system prompts, guardrails, and evaluation frameworks using tools like Ragas and LangSmith.
• Collaborate closely with cross-functional teams of data scientists, domain consultants, and solution architects to translate complex client business logic into AI systems.
• Establish automated evaluation pipelines to benchmark model output accuracy, hallucination rates, latency overhead, and computational inference costs.
• Automate cloud deployment, containerization, and continuous integration workflows utilizing Docker, Kubernetes, and AWS CI/CD pipelines.
• Monitor production model telemetry, token consumption, and system health to proactively detect data drift, latency spikes, and edge-case failures.
• Document system architecture patterns, technical specifications, and API integration contracts to support scalable knowledge sharing across engineering practices.

Requirements & Eligibility

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or an equivalent quantitative discipline.
• 0 to 1 years of professional experience, or demonstrable deep internship and portfolio project work in Generative AI, NLP, and machine learning engineering.
• Strong programming proficiency in Python with solid knowledge of object-oriented programming, data structures, and asynchronous execution.
• Hands-on practical experience developing applications with Generative AI frameworks such as LangChain, LlamaIndex, or CrewAI.
• Familiarity with cloud computing architectures, particularly AWS services including AWS Bedrock, SageMaker, Lambda, S3, and ECS.
• Working understanding of vector embeddings, similarity search algorithms, and vector databases such as Pinecone, ChromaDB, or FAISS.
• Knowledge of RESTful API development using FastAPI or Flask, and containerization fundamentals using Docker.
• Understanding of foundational LLM architectures, transformer mechanisms, tokenization, and prompt engineering best practices.
• Strong analytical problem-solving skills, intellectual curiosity to master emerging AI paradigms, and meticulous attention to code quality.
• Exceptional verbal and written communication skills to articulate technical AI concepts and trade-offs to cross-functional stakeholders.

Key Skills & Technologies

This role demands solid expertise in Python-based Generative AI engineering, modern LLM orchestration frameworks, and AWS cloud ecosystem services.

• Python & FastAPI: Primary programming language and high-performance framework used for building asynchronous GenAI microservices and APIs.
• AWS Bedrock & SageMaker: Managed cloud AI infrastructure leveraged for foundation model invocation, endpoint deployment, and inference scaling.
• LangChain & LlamaIndex: Orchestration frameworks applied to build dynamic LLM chains, agentic workflows, and semantic retrieval pipelines.
• Vector Databases (Pinecone / OpenSearch): High-dimensional indexing stores utilized for semantic document embedding and fast similarity retrieval.
• Prompt Engineering & Guardrails: Techniques and frameworks used to enforce model safety, deterministic outputs, and eliminate hallucinations.
• Docker & AWS ECS: Containerization and orchestration tooling applied to package, deploy, and scale microservices seamlessly.
• Git & CI/CD: Version control and deployment automation tools utilized for collaborative code maintenance and automated testing.

Why Join Fractal Analytics?

• Work at India's foremost AI-first unicorn analytics company, partnering with Fortune 500 global enterprises to solve complex enterprise challenges.
• Collaborate with elite data scientists, machine learning researchers, and AI strategists in an intellectual, high-growth engineering environment.
• Gain hands-on experience building enterprise-grade Generative AI products and proprietary platforms such as Qure.ai, Crux Intelligence, and Eugenie.ai.
• Thrive in a Great Place to Work® certified organization renowned for its people-first culture, employee well-being, and continuous learning investments.
• Access comprehensive corporate health insurance, performance-driven annual bonuses, and structured career development frameworks.

Expected Salary

Based on verified compensation benchmarks from Glassdoor, AmbitionBox, and industry analytics reports for entry-level AI Engineers and Data Scientists at Fractal Analytics in Bengaluru, the annual compensation typically ranges between ₹8,00,000 and ₹12,00,000 per annum. Total compensation packages also feature performance-based annual incentives, comprehensive medical coverage, and structured professional upskilling allowances. Final compensation offerings are calibrated according to candidate technical interview performance, hands-on GenAI project portfolios, and relevant academic background.