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Campus-Competent

HCLTech -
Greater Noida, Uttar Pradesh

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Job details

3 days ago

Qualifications

CI/CD
Azure
Law
Kubernetes
Spark
Git
Master's degree
Databases
Microservices
AWS
Docker
Distributed systems
Terraform
Continuous integration
GitHub
APIs
Kafka
AI
Jenkins
Communication skills
Python
Identity & access management

Full job description

| Greater Noida, Uttar PradeshBengaluru, Karnataka
Job Summary

Demand Unit Name : Generative AI Engineer II - LLM Application Engineering
Band : E2.1
Target : Engineering Graduates from Tier 1 Institutions (IIT, NIT, DTU)

Purpose of the Role:
The Platform Engineer II will design, build, and optimize scalable platforms that support Large Language Model (LLM) application development, deployment, and operations. This role enables rapid experimentation and reliable productionization of Generative AI solutions by creating robust infrastructure, automation pipelines, and integration frameworks. The engineer collaborates closely with AI/ML, application development, and DevOps teams to ensure high system reliability, security, and performance across the LLM lifecycle.

Key Responsibilities

Responsibilities :
Architect, implement, and enhance platform components supporting LLM-based application engineering, including model serving, vector databases, feature stores, and data pipelines.
Develop and maintain CI/CD automation for model deployment, evaluation, monitoring, and rollback.
Optimize infrastructure for performance, scalability, cost efficiency, and enterprise-grade security.
Integrate LLM services with internal tools, APIs, microservices, and cloud-native systems.
Build observability frameworks including logging, tracing, model performance metrics, and guardrail monitoring.
Support fine-tuning and RAG (Retrieval-Augmented Generation) workflows by enabling scalable data processing and model hosting.
Troubleshoot production issues, conduct root-cause analysis, and drive continuous platform improvements.
Collaborate with cross-functional teams to establish best practices, governance, and reusable components for LLM application delivery.
Contribute to documentation, knowledge sharing, and internal enablement around platform architecture and usage.
Work with stakeholders to identify key business problems and determine how AI can address them
Stay up-to-date with the latest AI trends and technologies, Responsible AI to continually enhance your skills and contributions.
Skill Requirements

Technical Skills

Must Have :
Strong programming skills in Python
Understanding of LLM Frameworks, RAG systems, GenAI, Agentic etc.
Strong experience in cloud platforms (AWS/Azure/GCP), including compute, networking, storage, and IAM.
Expertise in containerization and orchestration (Docker, Kubernetes, Helm).
Proficiency in building CI/CD pipelines (GitHub Actions, Azure DevOps, Jenkins, ArgoCD).
Solid understanding of LLMs, model serving frameworks (e.g., OpenAI, Hugging Face, TensorRT-LLM, LangChain), and RAG architectures.
Experience with infrastructure-as-code (Terraform, CloudFormation, ARM/Bicep).
Hands-on experience with monitoring/observability tools (Prometheus, Grafana, ELK, OpenTelemetry).
Familiarity with distributed systems, microservice architecture, and API design.
Experience with vector databases (FAISS, Pinecone, Milvus, Weaviate) and model hosting patterns.

Desirable :
Experience with MLOps platforms (SageMaker, Vertex AI, Azure ML).
Exposure to model optimization techniques (quantization, distillation, GPU tuning).
Understanding of data engineering tools (Spark, Databricks, Kafka).
Knowledge of security best practices for AI/ML systems (zero trust, data protection, compliance).
Experience contributing to internal developer platforms or platform engineering frameworks
Other Requirements

Behavioral Competencies :
Excellent Communication skills
Critical and Analytical Thinking
Strong problem-solving mindset with the ability to work in ambiguous and evolving environments.
Excellent collaboration and communication skills across cross-functional teams.
Adaptability and continuous learning orientation, especially in fast-moving AI technologies.
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