Senior AI/ML Engineer

Role Overview

You will design, build, deploy, and optimize large-scale AI/ML systems. The role demands end-to-end ownership: problem framing, data engineering, model development, evaluation, productionization, and continuous improvement. You?ll work closely with product, engineering, and business teams to translate ambiguous requirements into reliable, scalable AI solutions.

Key Responsibilities

1. Model Development & Research

• Build, fine-tune, and evaluate ML models (LLMs, transformers, classical ML, deep learning).

• Implement scalable architectures for training, inference, and monitoring.

• Perform literature reviews and incorporate state-of-the-art techniques into production systems.

• Optimize models for performance, latency, and cost.

2. Data Engineering & Pipelines

• Design and manage data pipelines for labeling, preprocessing, feature engineering, and validation.

• Ensure data quality, governance, lineage, and documentation.

• Work with large, unstructured datasets (text, audio, images, logs).

3. AI Infrastructure & Deployment

• Build training and inference pipelines using cloud services (AWS/GCP/Azure).

• Deploy models using Docker, Kubernetes, CI/CD pipelines.

• Implement monitoring: model drift, performance, anomalies, feedback loops.

4. Product & Cross-Functional Collaboration

• Translate product requirements into ML problem statements.

• Provide technical guidance to product managers, backend teams, and business stakeholders.

• Lead design discussions, architecture reviews, and experimentation strategy.

5. System Optimization

• Reduce inference costs using quantization, pruning, distillation, batching, caching.

• Drive latency improvements to meet real-time or near real-time requirements.

• Evaluate tradeoffs: accuracy vs. compute vs. complexity.

6. Mentorship & Leadership

• Mentor junior ML engineers and data scientists.

• Set engineering standards for experimentation, reproducibility, and documentation.

Required Skills & Experience

Core Technical Skills

• Strong proficiency in Python, PyTorch, TensorFlow, JAX, or similar.

• Deep understanding of LLMs, transformers, RAG, embeddings, vector databases.

• Expertise in classical ML: XGBoost, logistic regression, clustering, etc.

• Solid foundation in math, statistics, probability, optimization.

• Experience with ML Ops tools: MLflow, Weights & Biases, Airflow, Kubeflow, Ray.

Infrastructure & Tools

• Strong experience deploying models on AWS/GCP/Azure.

• Proficiency with Docker, Kubernetes, Terraform.

• Familiarity with microservices, API design, and scalable backend patterns.

• Hands-on with GPU compute, distributed training, and model parallelism.

Data Experience

• Hands-on with large-scale ETL, data cleaning, and feature engineering.

• Experience working with structured and unstructured data pipelines.

Soft Skills

• Ability to define ambiguous problems and derive measurable goals.

• Clear communication and documentation.

• Strong ownership and independent decision-making.

Preferred Qualifications

• Experience building AI agents, fine-tuning LLMs, or multimodal models.

• Publications or open-source contributions in ML/AI.

• Experience with retrieval systems, FAISS, LanceDB, or Pinecone.

• Understanding of A/B testing, causal inference, and experimentation frameworks.

Education

• Bachelor?s/Master?s/PhD in Computer Science, AI/ML, Data Science, or related fields.
  (Strong experience > formal degree.)

KPIs / Success Metrics

• Model performance: accuracy, latency, robustness, cost.

• Deployment stability & uptime.

• Reduction in inference costs through optimization.

• Successful delivery of end-to-end AI features.

• Internal adoption & documentation quality.

• Contribution to system architecture and team growth.