ML Engineer

Fresher

Save

Early Applicant

Job Description

Key Responsibilities

Design, develop, and deploy ML and LLM-powered applications into production
Build end-to-end ML & GenAI pipelines (data → evaluation → deployment → monitoring)
Develop agentic systems using LLMs (multi-step reasoning, tool usage, orchestration)
Implement RAG (Retrieval-Augmented Generation) architectures using vector search
Integrate LLMs with enterprise systems via APIs, tools, and function calling
Apply prompt engineering, model selection, and evaluation strategies
Optimize inference performance (latency, cost, throughput)
Implement MLOps + LLMOps practices (CI/CD, versioning, governance)
Monitor production systems for:
Response quality and hallucination risks
Data drift and prompt drift
Cost, latency, and token utilization
Build dashboards and monitoring solutions for ML/LLM systems (performance, usage, business KPIs)
Work with Databricks platform for data processing, feature engineering, and pipeline orchestration
Implement guardrails, safety filters, and responsible AI controls

Key Competencies

Strong systems thinking: ability to design production-grade AI systems
Ability to balance experimentation with cost, latency, and reliability constraints
Critical understanding of LLM limitations (hallucinations, grounding, context limits)
Ability to translate model outputs into business-facing insights via dashboards
Effective cross-functional collaboration