About NavaSys & the Role NavaSys Technologies is a technology consulting partner helping enterprises unlock value from AI, data, analytics and automation. As Technical Lead AI & Analytics Solutions , you'll be a key part of the core technology engine that: Defines how we architect and deliver AI/analytics solutions Sets the culture and standards for engineering across projects Represents NavaSys in high-stakes client conversations and presales Youll work closely with senior stakeholders, architects, engineers and consultants to build solutions that become reference points for future work inside NavaSys. Key Responsibilities Lead technical solutioning and architecture for AI/analytics projects, turning complex business requirements into robust, scalable designs. Anchor client workshops and strategic technology discussions with key decision makers and senior stakeholders, representing NavaSys AI and analytics capabilities. Drive presales engagements: contribute to client presentations, demos, solution walkthroughs, scoping calls, proposals and RFP responses. Collaborate with client teams to assess, design and deploy AI/ML models, advanced analytics systems and digital transformation initiatives. Design and implement modern data ecosystems, including: Cloud platforms: AWS, Azure, GCP Data lakes, data warehouses and ETL pipelines Data governance frameworks and policies Guide integration of analytics tools (Power BI, Tableau, Qlik, SAP BW) with enterprise systems to create actionable decision intelligence. Build, test and optimize data pipelines and models for predictive analytics, NLP, computer vision and process automation use cases. Coordinate delivery with data engineers, solution architects and business consultants, ensuring technology choices align with business outcomes and timelines. Mentor and coach junior developers and analysts, actively shaping the engineering culture at NavaSys. Continuously track AI/analytics technology trends, partner ecosystems and best practices, and bring them into NavaSys solutions in a pragmatic way. Ensure that all solutions adhere to enterprise-grade security, scalability, performance and compliance standards. Skills & Competency Areas Technical Solutioning & Architecture Data & AI Client Engagement & Communication Skills Presales & Proposal Development Leadership & Team Management Analytical & Problem-Solving Skills Project Management & Execution Ownership Industry & Partner Ecosystem Awareness Must-Have Qualifications Bachelors or Master’s degree 6+ years’ experience leading architecture and delivery of AI, ML and analytics solutions in enterprise environments. Significant client-facing presales experience: demos, workshops, solution pitches, proposals and RFP contributions. Deep expertise across the data ecosystem: Cloud: AWS / Azure / GCP Data warehousing & ETL Big Data technologies: Spark, Hadoop Strong hands-on proficiency with analytics & ML stack: Programming: Python, R, SQL BI tools: Power BI, Tableau, Qlik, SAP BW ML platforms: Databricks, SageMaker, Vertex AI Proven experience running client solution workshops and presenting to senior stakeholders. Strong understanding of business analytics, data architecture, data governance and AI transformation in large organizations. Solid project management (Agile/Scrum) and cross-functional collaboration experience. Excellent written and verbal communication skills. Experience working within enterprise security, regulatory and IT environments. Nice-to-Have Prior experience in an AI/analytics consulting or product organisation. Certifications in AI/ML, cloud data engineering or analytics platforms. Exposure to enterprise clients in CPG, BFSI, manufacturing or retail. Track record of speaking, publishing or community leadership in AI/data. Why Join NavaSys in This Role You’ll set the bar for how AI and analytics solutions are designed and delivered. You’ll have direct influence on culture, mentoring and technical standards across teams. You’ll work on high-visibility engagements with clear growth into practice and solution leadership roles.
About NavaSys & the Role At NavaSys, AI engineers are not just model builderstheyre end-to-end system owners who make AI real in production. As an AI Engineer – GenAI, Agentic AI & MLOps , you will: Build and deploy ML and Generative AI systems into production Design and orchestrate agentic AI workflows using modern frameworks Help define the engineering culture, tools and patterns for AI across NavaSys You’ll be part of a tight, high-calibre team that values clean engineering, experimentation and real-world outcomes. Key Responsibilities Design, develop and deploy machine learning and generative AI models into production environments. Build and integrate agentic AI systems—intelligent agents capable of reasoning, planning and multi-step decision-making. Develop and maintain data pipelines and MLOps workflows using Databricks, MLflow and cloud-native tooling. Integrate LLMs and AI agents with external APIs, databases and tools using frameworks such as LangChain, AutoGen, CrewAI, Semantic Kernel, LangGraph. Implement and manage Model Context Protocol (MCP) connections between AI agents and enterprise systems. Optimize AI workloads on AWS, Azure or GCP, ensuring they are performant, scalable, secure and cost-effective. Work with data, cloud and product teams to translate ideas into production-grade AI solutions rather than one-off POCs. Ensure security, observability, explainability and governance are first-class citizens in all AI systems you build. Core Skills AI/ML Engineer Python Machine Learning Engineer LLM LangChain MLflow MLOps Must-Have Capabilities AI / ML & Data Science Strong foundations in machine learning, deep learning and data science. Expertise in Python and ML libraries: PyTorch, TensorFlow, scikit-learn, pandas, NumPy. Understanding of model evaluation, feature engineering and transfer learning. Experience working with vector databases like FAISS, Pinecone, ChromaDB. Generative AI & NLP Hands-on experience with LLMs, prompt engineering, RAG (Retrieval-Augmented Generation) and fine-tuning. Familiarity with frameworks such as LangChain, LlamaIndex or similar orchestration tools. Experience implementing text generation, summarization, classification and document Q&A systems. Agentic AI, Agent Frameworks & MCP Strong understanding of agentic AI architectures (autonomous agents, tool use, planning, multi-step reasoning). Practical experience building AI agents using LangChain, AutoGen, CrewAI, LangGraph, Semantic Kernel or equivalent frameworks. Experience designing multi-agent collaboration and task orchestration. Hands-on experience with Model Context Protocol (MCP) for secure, robust tool invocation and context sharing. Awareness of safety, governance, auditability and agent evaluation frameworks. Databricks, MLOps & Data Engineering Solid experience with Databricks (Spark, Delta Lake, MLflow, feature store). End-to-end work on data pipelines, ETL/ELT and real-time streaming. Proficiency in MLOps best practices: model registry, versioning, drift detection, rollbacks. Experience implementing observability and automation for ML systems in production. Cloud & Infrastructure Hands-on experience deploying AI workloads on AWS, Azure or GCP. Familiarity with SageMaker, Azure ML or Vertex AI. Experience with Docker, Kubernetes and serverless paradigms. Working knowledge of Infrastructure as Code (Terraform / CloudFormation) and CI/CD pipelines. Software Engineering & APIs Strong software engineering fundamentals and coding discipline. Experience building REST / GraphQL APIs and microservices. Understanding of event-driven and asynchronous architectures (Kafka, Pub/Sub, message queues). Experience integrating AI components with existing enterprise systems. Security, Observability & Responsible AI Knowledge of monitoring, logging and tracing (Prometheus, Grafana, OpenTelemetry). Experience implementing secure AI practices (RBAC, secret management, prompt injection defenses). Understanding of model explainability, bias mitigation and ethical AI considerations. Nice-to-Have Familiarity with reinforcement learning and planning-based agents. Experience with knowledge graphs and symbolic reasoning. Exposure to multi-modal agents (text + vision + audio). Contributions to open-source AI or agent frameworks. Experience with edge or on-device AI. Certifications in Cloud AI / MLOps / Databricks / GenAI.
About NavaSys & the Role NavaSys is building a deep AI and data engineering capability that is secure-by-design and built to scale. As AI Architect, you will define how AI is structured, deployed and standardised across NavaSys projects. Youll combine broad architectural thinking with enough hands-on depth to validate approaches and guide teams confidently. Key Responsibilities Architect and design end-to-end AI/ML solutions from data ingestion and preparation to model deployment and monitoring. Work with business, product, data and engineering stakeholders to identify AI opportunities and automation use cases. Select and evaluate appropriate AI frameworks, platforms and tools (e.g., AWS SageMaker, Azure ML, GCP Vertex AI). Lead and mentor data scientists, ML engineers and developers on architecture, patterns and implementation choices. Ensure AI models and systems are scalable, secure, explainable and compliant with organisational and regulatory standards. Design MLOps pipelines for continuous integration, delivery and monitoring of models. Conduct POCs and feasibility studies to evaluate new AI technologies and techniques and define their adoption paths. Partner with data engineering to define data architecture, quality, governance and accessibility standards for AI use cases. Develop, maintain and evangelise AI architectural standards, design patterns and documentation that teams can reuse. Stay current on AI trends, tools and research and translate them into actionable architectural guidance. Preferred candidate profile Skills Required Artificial Intelligence / GenAI AI Solutions MLOps Machine Learning AWS GenAI Python Must-Have Profile Bachelors or Masters degree in Computer Science, Data Science, Artificial Intelligence or related field. Minimum 8 years of experience in AI/ML solution design and development, with at least 3 years in an architectural or leadership role. Strong expertise in machine learning, deep learning, NLP, computer vision and generative AI techniques. Proficiency in Python, R or Java and experience with ML/DL frameworks such as TensorFlow, PyTorch, Scikit-learn or Hugging Face. Hands-on experience with cloud platforms (AWS, Azure or GCP) and containerisation/orchestration (Docker, Kubernetes). Solid understanding of data engineering: ETL, data lakes, pipelines, real-time processing. Familiarity with MLOps tools (MLflow, Kubeflow, Airflow, CI/CD pipelines). Strong understanding of AI ethics, model interpretability and responsible AI principles. Excellent communication, documentation and leadership skills to influence cross-functional teams. Nice-to-Have Certifications such as AWS Certified Solutions Architect – Professional, Databricks Certified Data Engineer or TOGAF. Familiarity with AI/ML frameworks (TensorFlow, PyTorch, LangChain, etc.). Experience in pre-sales or customer-facing architecture roles. Exposure to data governance, AI ethics and responsible AI practices.