Infomineo

Analytics Engineer

• Lead the design and development of AI-powered analytical solutions, data products, and intelligent applications that solve complex business problems.
• Translate business and client requirements into data science approaches, AI workflows, and scalable technical solutions.
• Design, prototype, and productionize machine learning, LLM, and generative AI solutions with a focus on business value, reliability, and usability.
• Own the architecture of Retrieval-Augmented Generation (RAG) pipelines, including document processing, vectorization, semantic search, evaluation, and query optimization for enterprise use cases.
• Design and implement complex AI-powered features by integrating LLM APIs and services using frameworks such as LangChain or equivalent, with a focus on reliability, accuracy, and performance in production.
• Design, implement, and maintain Model Context Protocol (MCP) integrations to connect AI models with external tools, APIs, and data sources, enabling context-aware and extensible AI solutions.
• Develop evaluation frameworks, monitoring approaches, and observability practices for LLM-powered systems to ensure quality, transparency, and continuous improvement.
• Apply advanced prompt engineering, embedding strategies, and vector database management techniques to improve the performance of AI solutions.
• Integrate AI outputs into analytical workflows, dashboards, reporting tools, and client delivery pipelines.
• Design and contribute to the development of production-grade AI and data applications, primarily using Python and backend frameworks such as FastAPI.
• Build or support frontend interfaces using modern frameworks such as React, Next.js, or Vue to make AI and data products accessible to business users and clients.
• Collaborate with software engineers to define scalable application architectures, API standards, and integration patterns.
• Develop and maintain REST API integrations with third-party AI services, enterprise SaaS platforms, internal tools, and external data sources.
• Ensure that data science prototypes are translated into maintainable, secure, and scalable production solutions.
• Participate in code reviews, define technical best practices, and contribute to a high-quality engineering and data science culture.
• Support the containerization and cloud deployment of AI and data applications, preferably on Google Cloud Platform using GKE and Artifact Registry, while remaining adaptable to other cloud environments.
• Design and maintain CI/CD pipelines using GitHub Actions or equivalent tools to ensure reliable and repeatable releases.
• Apply MLOps and LLMOps practices to manage experimentation, deployment, monitoring, and continuous improvement of AI systems.
• Collaborate with engineering and infrastructure teams to ensure the reliability, scalability, and performance of production environments.
• Proactively identify performance bottlenecks in AI workflows, data pipelines, application layers, and infrastructure.
• Mentor junior data scientists, AI engineers, and developers on data science methods, AI integration, coding practices, and production readiness.
• Work closely with product teams, consultants, analysts, and non-technical stakeholders to ensure solutions are aligned with business needs.
• Define standards and best practices for AI solution design, evaluation, documentation, and delivery.
• Communicate complex technical concepts clearly to both technical and non-technical audiences.

Senior Software Engineer – AI & Analytics

Research Engineer

• Spearhead the development of next-gen solutions for our global clientele.
• Design and implement AI-driven solutions that automate tasks and boost efficiency across our entire service spectrum (Business Research, Content, Design, and Data Analytics).
• Collaborate with diverse, brilliant minds and gain exposure to a variety of industries and regions.
• Contribute your ideas and make a real difference in the company's growth.

Senior R&D Software Engineer

AI Engineer

• Lead the technical architecture of Data, Analytics and AI solutions for our clients, covering the full lifecycle from design to deployment:
• Design end-to-end data architectures: data lakes, lakehouses, warehouses, and streaming pipelines.
• Define standards for data modeling, storage, ingestion, and transformation across client engagements.
• Architect MLOps and AI deployment infrastructure (model registries, CI/CD for ML, monitoring).
• Lead technical decisions on cloud platforms (Azure, AWS, GCP) and open-source tooling.
• Define best practices and reusable frameworks for data engineers, analysts, and data scientists.
• Act as a technical mentor and reviewer for cross-functional project teams.
• Bridge the gap between data analysts, data engineers, and AI/ML engineers on complex projects.
• Contribute to internal knowledge base, toolkits, and delivery accelerators.
• Lead architecture workshops and discovery sessions with client stakeholders.
• Translate business requirements into scalable, robust technical blueprints.
• Present architecture decisions to both technical teams and executive audiences.
• Support pre-sales and proposal efforts with technical scoping and solution design.
• Provide internal training and knowledge-sharing sessions with the team.
• Support the Head of Practice on business development and internal capability initiatives.

Analytics & AI Architect

Data Analyst

• Design, implement, and operationalize data governance frameworks across client engagements, covering the following areas:
• **DATA GOVERNANCE & POLICY**
• Define and implement data governance frameworks, policies, and standards tailored to client environments.
• Establish data ownership, stewardship models, and accountability structures across business and technical teams.
• Develop and maintain data dictionaries, business glossaries, and classification taxonomies.
• Ensure compliance with data regulations and internal data management policies (GDPR, BCBS 239, etc.).
• **DATA QUALITY & LINEAGE**
• Design and implement data quality rules, profiling routines, and monitoring dashboards.
• Investigate and remediate data quality issues across structured and unstructured data sources.
• Map and document end-to-end data lineage to support auditability and impact analysis.
• Define and track data quality KPIs and SLAs in collaboration with data owners.
• **METADATA & CATALOGUING**
• Deploy and administer data catalog and metadata management tools (e.g. Informatica, Collibra, Alation, Microsoft Purview).
• Enrich metadata assets with business context, ownership, sensitivity classification, and usage information.
• Drive adoption of the data catalog across business and technical teams.
• **TOOLING & INTEGRATION**
• Configure and operate data governance platforms, primarily Informatica (IDMC, Axon, EDC, DQ) and equivalent tools.
• Integrate governance tooling with existing data pipelines, warehouses, and BI environments.
• Automate data quality checks and governance workflows within ETL/ELT pipelines.
• **OTHER**
• Provide internal training and knowledge-sharing sessions on data governance best practices.
• Support the Team Lead/Manager on client relationships, business development, and internal projects.

Senior Software Engineer – AI & Analytics

Senior R&D Software Engineer

Analytics & AI Architect

Data Governance Specialist, Freelance