AI Engineer

gehc; Karnataka; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

gehc in India, Karnataka seeks an experienced AI Engineer to design, develop, and deploy intelligent systems leveraging traditional AI and GenAI technologies. You will work across the AI lifecycle—from data engineering and model development to deployment and monitoring—building agentic platforms and scalable AI solutions.

The ideal candidate has 4+ years of AI/ML experience, strong Python proficiency, and hands-on experience with GenAI tools, AWS SageMaker/Bedrock, and MLOps.

Qualifications

• Bachelor's degree in engineering or equivalent.
• 4+ years of relevant AI/ML experience.
• Strong knowledge of GenAI and LLMs.

Responsibilities

• Develop and fine-tune Generative AI models (LLMs, diffusion models).
• Build scalable AI pipelines for data ingestion and training.
• Deploy models to production with MLOps tooling.
• Collaborate with cross-functional teams to translate business needs into AI solutions.
• Ensure model performance, fairness, and explainability.
• Monitor deployed models and optimize performance and costs.
• Stay updated on AI/GenAI research and assess applicability.
• Document experiments and share knowledge across teams.

Skills

GenAI Platforms

LLMs

Prompt Engineering

MLOps

Python

System Design

Education

Bachelor's degree in engineering

Tools

AWS SageMaker

Bedrock

SageMaker JumpStart

LangChain

FAISS

Pinecone

Weaviate

OpenSearch

Job description

Job Description Summary

We are seeking a highly skilled and innovative AI Engineer with expertise in both traditional Artificial Intelligence and emerging Generative AI technologies. In this role, you will be responsible for designing, developing, and deploying intelligent systems that leverage machine learning, deep learning, and generative models to solve complex problems. You will work across the AI lifecycle-from data engineering and model development to deployment and monitoring-while also exploring GenAI applications, Agentic AI and developing agentic platforms. The ideal candidate combines strong technical acumen with a passion for experimentation, rapid prototyping, and delivering scalable AI solutions in real-world

Job Description

Roles and Responsibilities

In this role, you will: ( 3 - 5 yrs exp)

• Develop and fine-tune Generative AI models (e.g., LLMs, diffusion models).
• Design and implement machine learning models for classification, regression, clustering, and recommendation tasks.
• Build and maintain scalable AI pipelines for data ingestion, training, evaluation, and deployment.
• Collaborate with cross-functional teams to understand business needs and translate them into AI solutions.
• Ensure model performance, fairness, and explainability through rigorous testing and validation.
• Deploy models to production using MLOps tools and monitor their performance over time.
• Stay current with the latest research and trends in AI/ML and GenAI and evaluate their applicability to business problems.
• Document models, experiments, and workflows for reproducibility and knowledge sharing.

Technical Skill Set

Cloud & Infrastructure (AWS)

• Amazon SageMaker - Model training, tuning, deployment, and MLOps.
• Amazon Bedrock - Serverless GenAI model access and orchestration.
• SageMaker JumpStart - pre-trained models and GenAI templates.
• Prompt engineering and fine-tuning of LLMs using SageMaker or Bedrock.

Programming & Scripting

• Python - Primary language for AI/ML development, data processing, and automation.

Education Qualification

Bachelor's degree in engineering with minimum four years of experience in relevant technologies.

Desired Characteristics

Technical Expertise:

• 1. GenAI Platforms & Models
  • Familiarity with LLMs: like Claude (Anthropic), LLaMA (Meta), Gemini (Google), Mistral, Falcon
  • Experience with APIs: Amazon Bedrock.
  • Understanding of model types: encoder-decoder, decoder-only, diffusion models
  • Design, develop, and deploy agent-based AI systems that exhibit autonomous decision-making.
  • Integrate Generative AI (LLMs, diffusion models) into real-world applications.
• 2. Prompt Engineering & Fine-Tuning
  • Prompt design for zero-shot, few-shot, and chain-of-thought reasoning
  • Fine-tuning and parameter-efficient tuning (LoRA, PEFT)
  • Retrieval-Augmented Generation (RAG) design and implementation
• 3. System Integration & Architecture
  • Event-driven and serverless architectures (e.g., AWS Lambda, EventBridge)
• 4. Development Frameworks
  • LangChain, LlamaIndex.
  • Vector databases: FAISS, Pinecone, Weaviate, Amazon OpenSearch
  • Langgraph, Langchain
• 5. Cloud & DevOps
  • AWS (Bedrock, SageMaker, Lambda, S3), Azure (OpenAI, Functions), GCP (Vertex AI)
  • CI/CD pipelines for GenAI workflows
• 6. Security & Compliance
  • Data privacy and governance (GDPR, HIPAA)
  • Model safety: content filtering, moderation, hallucination control
• 7. Monitoring & Optimization
  • Model performance tracking (latency, cost, accuracy)
  • Logging and observability (CloudWatch, Prometheus, Grafana)
  • Cost optimization strategies for GenAI inference
• 8. Collaboration & Business Alignment
  • Working with product, legal, and compliance teams
  • Translating business requirements into GenAI use cases
  • Creating PoCs and scaling to production

Additional Information

Relocation Assistance Provided: No