Jobs at Sia Partners

Curates, classifies, tags, and maintains strategic knowledge assets from internal and external sources. Ensures metadata consistency, data quality, confidentiality, governance compliance, repository hygiene, and version control. Uses AI-enabled tools such as NLP, semantic search, document classification, and automated tagging to improve processing and retrieval. Supports consulting teams, generates repository reporting and dashboards, and contributes to knowledge management standards and continuous improvement.

Leads software and cloud engineering teams building scalable AI and generative AI SaaS products. Owns technical roadmaps, cloud architecture, distributed systems, microservices, CI/CD, infrastructure, reliability, and cost optimization. Partners with product, research, data science, and operations teams to deliver production-grade AI capabilities. Provides architectural leadership, contributes targeted code, develops engineering talent, and communicates technical trade-offs to clients and stakeholders.

Architect and standardize multi-cloud DevOps and platform foundations for AI products. Own CI/CD, Kubernetes, infrastructure automation, observability, reliability engineering, security, and cloud cost governance across teams. Serve as the escalation point for complex infrastructure incidents, guide architecture and deployment practices, improve developer experience, and mentor senior engineers without direct people management.

Develop and optimize Python backend services, microservices, and data-centric applications for AI-powered SaaS products. Integrate and optimize AI/ML models, manage PostgreSQL and MongoDB databases, and deploy applications using Docker, Kubernetes, Terraform, and Helm. Work across GCP, AWS, and Azure with CI/CD pipelines, testing, and artifact management. Collaborate with DevOps, data scientists, and data engineers while contributing to frontend and overall software architecture and mentoring engineering teams.

Build and own scalable AI and Generative AI platforms using Python, microservices, cloud infrastructure, and machine learning models. Design backend architecture, deploy and optimize ML systems, manage Kubernetes and Terraform-based infrastructure, maintain databases and observability platforms, and lead production reliability practices. The role also involves technical leadership, mentoring, architecture decisions, incident response, and translating client requirements into robust technical solutions.

Embed with client engineering teams to translate requirements into production software. Build Python backend services, APIs, integrations, RAG pipelines, tool-calling agents, and LLM workflows. Deliver platform improvements and AI use cases while applying testing, CI/CD, monitoring, security, and reliability practices. Communicate technical decisions and risks, document solutions, and transfer operational knowledge to client teams.

Build and deploy enterprise Generative AI applications using LLMs, RAG pipelines, vector databases, multimodal models, and autonomous agents. Develop prompt flows, benchmark and fine-tune models, implement MLOps practices, deploy solutions across major cloud platforms, and integrate AI capabilities into APIs and microservices. Collaborate with consultants, data scientists, engineers, architects, and customers to deliver secure, scalable, compliant, and cost-effective GenAI solutions.

Administer and enhance Meraki and UniFi network infrastructure across international offices, including firewalls, switches, access points, cabling, and office deployments. Manage Microsoft 365, Entra ID, Intune, and Azure environments; provide advanced network and application support; automate administration using PowerShell, Python, and Microsoft Graph API; support data migrations, technical documentation, and IT integration during acquisitions. Occasional international travel is required.

Provide global Level 1 and Level 2 technical support through ticketing, phone, Teams, and on-site channels. Diagnose hardware, software, network, account, and access issues; configure Windows and Mac workstations; manage devices, inventory, and lifecycle activities; document resolutions; meet ITSM and SLA requirements; support VIP users; and collaborate with international IT teams in a 24x7 rotational shift environment.

The DevOps Engineer supports consulting engagements, SaaS product development, and data-focused projects. Responsibilities include managing cloud architecture and services, optimizing costs and access, containerizing and orchestrating applications with Docker and Kubernetes, developing Python automation tools and microservices, implementing CI/CD pipelines, and contributing to scalable technology, architecture, and governance decisions.

Own end-to-end recruitment for technical, consulting, and corporate roles across India and global locations. Develop sourcing strategies, build talent pipelines, conduct screening interviews, coordinate assessments, maintain ATS records, and support hiring teams. Provide talent market intelligence on compensation, sourcing challenges, and competitors, while managing career-fair outreach and delivering a strong candidate experience from sourcing through offer.

Design, build, and maintain ETL pipelines, semantic models and SQL transformations. Develop and optimize Power BI dashboards (DAX, Power Query, RLS), lead end-to-end BI projects, troubleshoot performance, document data lineage, and mentor junior analysts to enforce BI best practices.

Lead automation testing and QA activities within an agile team: design and execute test automation, produce secure high-quality code, create architecture artifacts, analyze large datasets to improve systems, identify hidden issues, and contribute to engineering communities and inclusive team culture.

Design, build, and maintain Power BI dashboards and reusable tabular data models; administer Power BI Service; ingest and transform data using SQL/Python and ETL tools; support stakeholders with ad-hoc reporting, documentation, demos, and Power Platform requests.

Lead architecture, development, and deployment of GenAI backend systems (LLMs, RAG, agentic workflows). Build scalable microservices, data pipelines, and cloud-native deployments (AWS/Azure/GCP) using Docker, Kubernetes, and Terraform. Ensure production readiness, monitoring, cost control, and security; collaborate with data scientists, mentor engineers, and engage in client-facing consulting and technical leadership.

Lead and grow a cross-functional AI platforms team to productionize LLM-based solutions. Own platform roadmap, architecture, MLOps pipelines, vector DB integrations, security/compliance, and cross-functional alignment to deliver scalable, reliable GenAI services.

The Senior Applied Data Scientist will lead the design and implementation of analytical solutions, translating business requirements into scalable data products while mentoring junior scientists and ensuring model deployment success.