AI/ML Engineer — Job Posting

Key Responsibilities

1. Model Development & Training

• Build, train, and fine-tune ML models including LLMs, NLP systems, and speech-to-text models
• Apply modern techniques like LoRA, QLoRA, DPO, RLHF, and adapters
• Design high-quality training datasets through cleaning, annotation, and augmentation
• Run experiments, ablations, and performance evaluations to improve model quality

2. Real-Time Model Deployment

• Build low-latency inference pipelines for real-time AI responses
• Optimize performance using streaming, caching, batching, and quantization
• Deploy models using Docker, Kubernetes, and CI/CD pipelines
• Integrate multiple LLM providers with routing, failover, and orchestration systems

3. RAG & AI Systems

• Build retrieval-augmented generation (RAG) pipelines using embeddings and vector databases
• Develop semantic search, summarization, classification, and NLP features
• Design prompt systems, tool calling, and multi-turn conversational flows
• Build agentic workflows with function calling and multi-step reasoning

4. Evaluation & Monitoring

• Create automated and human evaluation systems for model quality
• Track latency, hallucinations, accuracy, cost, and user satisfaction metrics
• Implement drift detection and regression monitoring
• Optimize token usage and inference costs across models

5. Data & Feature Engineering

• Build scalable data pipelines for training and production use
• Design feature extraction systems from user interactions and logs
• Implement data versioning, lineage tracking, and experiment tracking
• Collaborate with analytics and engineering teams on data infrastructure

6. Safety & Responsible AI

• Implement guardrails, filters, and safety validation systems
• Evaluate models for bias, fairness, and robustness
• Ensure privacy-first design and secure handling of user data
• Support responsible AI practices across the ML lifecycle