About SiriusAI Sirius AI is a US headquartered AI Consulting services and products company with operations in India. Sirius AI focuses on Financial Services enterprises and solutions / services delivered across multiple geographies. We are an innovation-driven AI and data driven consulting services firm with a high focus on delivering business outcomes for our clients. The Sirius AI team is a mix of talent from a diverse background of Consulting, Industry and Product companies which bring together their Data, Platform, Ecosystem, Technology, Project management and AI skills to develop new solutions and deliver client engagements. Role Overview We are looking for a highly skilled Senior Consultant
- ML Engineering with 2-4 years of experience to design, develop, and deploy enterprise-scale AI/ML and Generative AI solutions for consulting clients across Financial Services and other industries. The ideal candidate will combine strong backend engineering expertise in Python with deep knowledge of AI/ML frameworks, large language models, and cloud-native architectures, and will collaborate closely with data science, frontend, DevOps, and client-facing consulting teams.

Key Responsibilities
• Design, develop and deploy end-to-end Machine Learning, Deep Learning, LLM/Agents-based solutions, including classification, regression, fraud detection, anomaly detection, forecasting and predictive modelling.
• Build and operationalize end-to-end ML pipelines covering data ingestion, feature engineering, model training, evaluation, deployment and monitoring.
• Develop enterprise GenAI applications using LLMs, RAG, embeddings, prompt engineering, fine-tuning and agentic AI frameworks.
• Implement MLOps and LLMOps practices covering experiment tracking, model versioning, CI/CD, model registries, monitoring and reproducibility.
• Build scalable Python backend services, REST APIs and microservices using FastAPI, Flask or Django.
• Deploy and manage AI/ML workloads across AWS, Azure or GCP,



leveraging containerization, Kubernetes and cloud-native AI services.
• Work with GPU-enabled infrastructure and distributed computing for model training, inference and performance optimization.
• Design and optimize data processing and pipeline orchestration workflows using PySpark, Airflow, Kubeflow or managed cloud services.
• Develop secure, scalable and reliable AI systems with appropriate observability, access controls, governance and cost optimization.
• Collaborate with cross-functional and client-facing teams on solution architecture, technical design, POCs and enterprise delivery.
• Drive engineering best practices, technical reviews, code quality and mentoring of team members. Required Skills & Experience
• Strong Python and SQL skills, with production-quality coding, Git-based development practices, hands-on experience with PyTorch, TensorFlow and Scikit-learn, FastAPI, Flask or Django, REST APIs.
• Train, fine-tune, and deploy machine learning models where they outperform off-the-shelf solutions
• Strong understanding of ML algorithms, statistical modelling, feature engineering, model evaluation and optimization, also including model deployment, serving, monitoring, observability, and retraining in production
• Rapidly prototype and validate new ideas, then harden the successful ones into production-grade systems
• Understanding of transformer-based models, distributed or multi-GPU training, checkpointing, mixed precision, data loading, model serving and performance optimization for transformer-based models.




• Practical experience building LLM-powered applications using RAG, embeddings, vector databases and prompt engineering.
• Experience deploying scalable AI applications using containerization and cloud-native services and practical knowledge of cloud compute, storage, and GPU-enabled AI workloads across AWS, Azure or GCP.
• Experience designing and operating training, evaluation, batch-scoring and deployment pipelines using Airflow, Kubeflow, MLflow, Azure ML, AWS SageMaker or Vertex AI.
• Hands-on experience with Docker, CI/CD, model registries, experiment tracking and reproducible environments.
• Familiarity with agentic AI frameworks such as LangGraph, LangChain, LlamaIndex or Google ADK. Positive to Have
• Experience in Financial Services use cases such as credit risk, fraud detection, AML, transaction monitoring and customer analytics.
• Exposure to Foundation Models, domain-specific model development, fine-tuning and GPU infrastructure.
• Experience building production grade multi-agent systems (task delegation, coordination, and autonomous decision-making)
• Use AI coding tools and agents throughout design, development, testing, debugging, and code review.
• Experience with computer vision, reinforcement learning and continuous self-learning.
• Familiarity with big data technologies (Spark, Databricks, Snowflake)
• Knowledge of responsible AI, AI safety, and model explain ability.

What We Offer
• Opportunity to build cutting-edge AI, GenAI, Agentic AI and Foundation Model solutions for global enterprises.
• Exposure to complex, high-impact Financial Services AI transformation initiatives.
• Opportunity to work on enterprise AI platforms, reusable accelerators and next-generation AI products.
• High ownership, technical challenges and collaboration with experienced AI architects and technology leaders.
• Competitive compensation, continuous learning and accelerated career growth.

📌 Senior Consultant (Gurugram)
🏢 SiriusAI
📍 Gurugram