AI/ML Engineer

Job Summary

We are looking for a experienced AI/ML Engineer to design, build, and deploy machine learning models and intelligent systems that solve real business problems at scale. This role has a strong focus on modern generative AI - building LLM-powered applications, RAG pipelines, and agentic AI systems - alongside classical ML. You will work across the full ML/AI lifecycle, from data exploration and model development to production deployment and monitoring, partnering closely with Data Science, Product, and Platform Engineering teams. This role suits someone who enjoys turning research and prototypes into reliable, production-grade systems.

You will be reporting to Team Lead-IT.

Your Responsibilities

• Design and build LLM-powered applications, RAG pipelines, and agentic AI workflows to automate and augment business processes.
• Stay current with the latest ML/AI research (including LLMs, RAG, and agentic systems) and evaluate applicability to company use cases.
• Document architectures, experiments, and decisions; contribute to internal ML standards, code reviews, and knowledge sharing.
• Ensure responsible AI practices - fairness, explainability, privacy, and security - are embedded throughout model development
• Build and maintain robust, scalable data pipelines for feature engineering, model training, and inference (batch and real-time).
• Deploy models into production using MLOps best practices - CI/CD for ML, containerization, model versioning, and automated retraining pipelines.
• Monitor deployed models for drift, performance degradation, bias, and data quality issues; implement retraining and alerting strategies.
• Collaborate with Data Engineers to ensure clean, reliable, and well-governed data is available for model development.
• Partner with Product Managers and stakeholders to translate business requirements into ML solutions and model capabilities, limitations, and trade-offs.
• Conduct experiments (A/B tests, offline evaluation) to validate model performance and business impact.
• Optimize models for latency, throughput, and cost (quantization, distillation, pruning, efficient inference serving).

The Essentials - You Will Have

• Bachelors degree in Computer Science, Data Science, Statistics, Mathematics, Machine Learning, or a related field (or equivalent practical experience).
• 2-5+ years of hands-on experience building and deploying machine learning models in production (adjust based on seniority: 5-8+ years for Senior/Staff level).

Technical Skills

• Strong programming skills in Python; familiarity with SQL
• Proficiency with ML/DL frameworks: TensorFlow, PyTorch, scikit-learn, XGBoost/LightGBM.
• 5+ years of experience with the ML lifecycle: data preprocessing, feature engineering, model training, hyperparameter tuning, evaluation, and deployment.
• Hands-on experience with MLOps tooling: MLflow, Kubeflow, SageMaker, Vertex AI, Airflow, or similar for experiment tracking, orchestration, and deployment.
• Solid understanding of statistics, probability, linear algebra, and core ML/DL algorithms.
• Experience with cloud platforms (Azure) and containerization/orchestration (Docker, Kubernetes).
• Familiarity with distributed data processing (Spark, Ray, Dask) and data pipeline/orchestration tools (Airflow, Dagster).
• 5+ years of experience with model serving and APIs (FastAPI/Flask, TorchServe, Triton Inference Server, or similar).
• Version control (Git) and CI/CD practices for ML systems.

Generative AI, LLMs & Agentic Applications (Essential)

• Hands-on experience building applications powered by Large Language Models (LLMs) - e.g., OpenAI, Anthropic Claude, Llama, Mistral, or Gemini APIs.
• Practical experience designing and implementing Retrieval-Augmented Generation (RAG) pipelines, including chunking strategies, embeddings, and retrieval evaluation.
• Experience building agentic AI systems / multi-step autonomous workflows (tool use, function calling, planning, orchestration) using frameworks such as LangChain, LlamaIndex, or similar.
• Working knowledge of vector databases (Pinecone, Weaviate, FAISS, Milvus, Chroma) for semantic search and retrieval at scale.
• Experience with prompt engineering, evaluation, and fine-tuning/adapting LLMs (LoRA, PEFT) for domain-specific use cases.
• Understanding of the trade-offs, latency, cost, and safety considerations of deploying LLM- and agent-based applications in production.

The Preferred - You Might Also Have

• Experience deploying and scaling multi-agent systems in production (orchestration, memory, guardrails, observability for agentic workflows).
• Contributions to open-source LLM/RAG tooling, or published work on GenAI applications.
• Publications, open-source contributions, or Kaggle competition experience.
• Experience with real-time/streaming ML systems (Kafka, Flink, Spark Streaming).
• Domain experience relevant to the business (e.g., fintech, healthcare, e-commerce, cybersecurity).
• Understanding of responsible AI, model interpretability (SHAP, LIME), and privacy-preserving ML techniques.

Tools & Technology (Representative)

• Languages: Python, SQL
• ML/DL: PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers
• GenAI / LLM: OpenAI, Anthropic Claude, LangChain, LlamaIndex, RAG pipelines, vector DBs (Pinecone/Weaviate/FAISS)
• MLOps: MLflow, Kubeflow, SageMaker/Vertex AI/Azure ML, Airflow
• Data: Spark, Pandas, Snowflake/BigQuery/Redshift
• Infra: Docker, Kubernetes, AWS/GCP/Azure, Terraform
• Monitoring: Prometheus/Grafana, Evidently AI, WhyLabs

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.