GenAI Engineer

Hiringhood; Bengaluru; Hybrid; INR 1,200,000 - 1,800,000; Full time

Job summary

Hiringhood is seeking a GenAI Engineer with 4–8 years of experience to design, build, and deploy GenAI and AgenticAI applications. You will work on Python-based backends, LLM orchestration, RAG pipelines, and observability tools, delivering production-grade AI solutions in a hybrid setup.

The role emphasizes end-to-end development, collaboration with cross-functional teams, and hands-on implementation of scalable AI infrastructure for industrial tech challenges.

Qualifications

• 4–8 years of experience in Data Science, ML, AI engineering, or GenAI development.

Responsibilities

• Own end-to-end GenAI/AgenticAI solutions from prototype to production.
• Build and deploy GenAI apps in production with scalable backend.
• Collaborate with Product and Engineering teams to translate problems into scalable AI solutions.

Skills

GenAI experience

Agentic AI

LangChain

LangGraph

AutoGen

REST APIs

gRPC

Python

LLM orchestration

Time-series modeling

CI/CD

Distributed systems

Production design

Observability tools

LangSmith

Embeddings

RAG

Education

Bachelor’s degree in CS/IT/Engineering

Tools

REST

gRPC

Job description

Work Mode: Hybrid – 3 Days Work from Office

Experience: 4–8 Years

Employment Type: Full Time

Notice Period: Immediate to 15 Days

Open Positions: 1

About the Company

We are a growing technology company building innovative AI, GenAI, and intelligent industrial technology solutions. The organization focuses on developing scalable AI-powered products that combine Generative AI, Agentic AI, machine learning, IoT, time-series data, and modern cloud technologies to solve complex real-world business and operational challenges.

About the Role

We are looking for a GenAI Engineer with 4–8 years of experience across Data Science, Machine Learning, AI Engineering, or GenAI development. The ideal candidate will have hands‑on experience building and deploying GenAI and AgenticAI applications in production, with strong expertise in Python, LLMs, RAG, AI agents, orchestration frameworks, and scalable backend systems.

Key Skills

• 4–8 years of experience in Data Science, Machine Learning, AI Engineering, or GenAI
• Hands‑on experience building and deploying GenAI / AgenticAI applications
• Strong experience with LangChain, CrewAI, AutoGen, LangGraph, or similar frameworks
• Strong understanding of:
• Embeddings
• RAG
• LLM orchestration
• AI agents
• LLM evaluation
• Experience with time-series modeling
• Knowledge of HITL, LLM-as-a-Judge, deterministic evaluation, and fine‑tuning workflows
• Experience with LangSmith or equivalent observability/experimentation tools
• Strong proficiency in Python
• Experience building REST and/or gRPC APIs and microservices
• Understanding of CI/CD, distributed systems, and production system design
• Experience optimizing AI inference performance, latency, and API costs
• Understanding of cloud-native development and scalable AI infrastructure

Good to Have

• Experience in industrial systems, IoT, digital twins, or manufacturing technologies
• Time‑series modeling, anomaly detection, predictive maintenance, or sensor analytics
• Kafka, NSQ, or similar event‑driven technologies
• Databricks, BigQuery, Snowflake, or lakehouse architectures
• Knowledge graphs or context graphs
• Experience working with multimodal AI involving structured, unstructured, and sensor‑driven data
• Own the end‑to‑end development of GenAI and AgenticAI solutions, from prototyping to production deployment and monitoring
• Build intelligent systems combining time‑series modeling, signal processing, LLMs, embeddings, agents, and retrieval pipelines
• Design and implement scalable RAG pipelines, tool‑calling workflows, multi‑agent systems, and evaluation frameworks
• Develop AI applications integrating sensor‑based time‑series data, unstructured text, and machine learning outputs
• Drive technical decisions around architecture, tooling, experimentation, and deployment
• Collaborate with Product, Engineering, Applied AI, and domain teams to translate customer problems into scalable solutions
• Contribute to data pipelines, deployment infrastructure, evaluation frameworks, MLOps, and LLMOps
• Build backend services using Python, including REST/gRPC APIs and workflow orchestration services
• Monitor AI systems for performance, drift, reliability, latency, and cost efficiency
• Continuously improve model quality and production scalability
• Collaborate with customers and internal stakeholders to prototype and deliver AI‑driven solutions

Education

Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related technical field (B.Tech / B.E. or equivalent).

Interview Process

L1: Technical Discussion L2: Coding & Technical Discussion – 1.5 Hours

Note: L2 is Face-to-Face and candidates are required to bring their own laptop for the coding round.