Overview
Skills
Job Details
We are seeking a highly experienced AI Senior Engineer to design, build, and optimize advanced AI and machine learning solutions for large-scale enterprise environments. This role involves hands-on development of ML models, LLM-based applications, automation workflows, and AI-driven operational systems. The ideal candidate will have strong engineering expertise in Python, ML frameworks, data pipelines, cloud platforms, and production-grade AI deployment.
You will work closely with architecture, data engineering, DevOps/SRE, and product teams to deliver intelligent, scalable, and high-performing AI solutions.
Key Responsibilities
AI & Machine Learning Development
Design, develop, train, and optimize ML models, including classical ML, deep learning, and LLM-based solutions.
Implement time-series forecasting, anomaly detection, NLP pipelines, predictive analytics, and recommendation systems.
Build scalable training and inference workflows using modern ML frameworks (TensorFlow, PyTorch, Scikit-learn).
Optimize model performance, latency, and accuracy for production workloads.
Generative AI & LLM Engineering
Build applications using LLMs (OpenAI, Hugging Face, Azure OpenAI, etc.).
Develop LLM-based agents, chatbots, automation assistants, and knowledge retrieval systems.
Implement prompt engineering, fine-tuning, RAG pipelines, vector stores, and embeddings.
Ensure secure, governed, and compliant integration of LLM-based systems.
AI Platform & MLOps Engineering
Build and manage end-to-end ML pipelines, including data ingestion, feature engineering, model training, and deployment.
Work with ML Ops tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Databricks.
Implement model monitoring, drift detection, retraining automation, and CI/CD for ML systems.
Ensure scalable, resilient, and cost-optimized ML infrastructure in cloud environments.
Data Engineering & Processing
Work with large, complex datasets using Spark, SQL, Python, and distributed data systems.
Develop automated pipelines for data preprocessing, quality checks, transformations, and feature stores.
Collaborate with data engineering teams to improve data availability, lineage, and reliability.
AI Ops & Intelligent Automation
Build AI-driven operational tools for anomaly detection, noise reduction, auto-remediation, and predictive observability.
Integrate AI insights into SRE/DevOps workflows, dashboards, and automation frameworks.
Accelerate operational efficiency using AI to reduce MTTR and prevent incidents.
Cloud Engineering
Deploy and manage AI workloads using cloud-native services in AWS, Azure, or Google Cloud Platform.
Utilize serverless, containerized, and distributed compute systems for training and inference.
Optimize cloud resource usage for performance and cost.
Security, Compliance & Responsible AI
Apply enterprise-level security, access control, and governance for AI/ML systems.
Implement responsible AI principles including bias detection, explainability, transparency, and auditability.
Ensure privacy-preserving model operations and data protection.
Collaboration & Leadership
Work closely with architects and cross-functional teams to align AI solutions with enterprise strategy.
Mentor junior engineers and contribute to engineering best practices.
Document systems, design decisions, models, and operational workflows clearly and effectively.
Required Skills & Experience
6 12+ years of experience in AI/ML engineering.
Strong programming skills in Python, with expertise in:
PyTorch, TensorFlow, Scikit-Learn
NLP and LLM frameworks
Data processing libraries (Pandas, NumPy, Spark)
Hands-on experience with ML Ops tools and cloud AI services.
Experience training and deploying ML/LLM models in production.
Strong understanding of distributed systems, APIs, microservices, and containerization (Docker/Kubernetes).
Experience with data engineering pipelines, ETL processes, and SQL.
Solid understanding of model monitoring, drift management, and performance tuning.
Preferred Qualifications
Cloud certifications (AWS, Azure, or Google Cloud Platform AI/ML).
Experience building RAG, vector search, and embedding-based solutions.
Experience in large-scale automation, AI Ops, or SRE environments.
Experience with enterprise security and compliance frameworks.
Background working in financial