Codvo.ai

Full-stack Engineer

• Act as the technical lead for a scrum team building software
• Design and evolve end-to-end architecture
• Lead integration with internal teams
• Provide technical guidance to engineers

Senior Technical Lead

AI Engineer

• Design and build AI agent pipelines and maintain RAG systems
• Integrate and optimize Large Language Models and structured output workflows
• Design, develop, and deploy product features and scalable backends
• Collaborate with clients and stakeholders to translate business requirements into software solutions
• Own observability and quality metrics for AI systems

Senior AI Engineer – Full-Stack

SDET

• Coordinate QA activities across all Neio Project products
• Own QA planning, work allocation, and test execution tracking
• Guide QA resources on test coverage and automation standards
• Review test cases and execution results
• Ensure timely QA sign-off inputs for releases

Senior QA Automation Engineer, AI

Product Manager

• Own the vision, roadmap, and strategy for NeIO GIS product line.
• Identify high-value use cases across GIS workflows such as: Spatial analytics & querying, Asset mapping & monitoring, Network planning & optimization, Land management, utilities, O&G, infrastructure use cases
• Bring deep understanding of: GIS data models (layers, features, spatial queries) and Platforms like ArcGIS, QGIS, PostGIS, Mapbox, etc
• Define and manage product backlog, feature roadmap, and release plans.
• Work closely with Engineering, AI, and Design teams to: Define clear requirements, Drive execution, Ensure scalability and usability
• Prioritize features based on: Business value, Customer demand, Technical feasibility
• Ensure production readiness, UAT validation, and quality of releases.
• Define product positioning, ICP, and value proposition
• Lead implementation and deployment of GIS solutions for enterprise clients. Ensure: Clear requirement capture, Alignment between product and client needs, Smooth execution and delivery
• Oversee UAT, client feedback, and production rollout.
• Act as a bridge between product and delivery teams.
• Stay updated with trends in: GIS & geospatial technologies, AI in spatial analytics, Industry-specific GIS applications (O&G, utilities, telecom, etc.)

Product Lead – GIS, Geospatial AI

Data Scientist

• Train and optimize Deep Neural Network architectures for Regression and Industrial Control use cases
• Design and implement end-to-end Machine Learning and Deep Learning pipelines including data ingestion, data cleaning, feature engineering, model training, deployment, and monitoring
• Work with large and complex datasets for preprocessing, exploratory data analysis (EDA), and feature engineering
• Apply Regression Models and Predictive Analytics to solve business problems
• Develop Time Series and Forecasting models for trend prediction and risk estimation
• Collaborate with cross-functional teams to integrate ML solutions into production environments

Data Scientist, India

• Design and execute comprehensive test cases covering all reconciliation scenarios—matched, missing, duplicate, mismatch, and unmatched records—ensuring end-to-end accuracy across the platform
• Create robust test data sets that capture edge cases, boundary conditions, and real-world financial scenarios to validate system behavior under diverse conditions
• Perform regression testing after each build to identify regressions and ensure previously fixed issues remain resolved
• Validate reconciliation outputs by manually verifying Excel and PDF reports against expected results, with sharp focus on amount discrepancies, date accuracy, and ID-level matching
• Develop and maintain automated test frameworks using pytest or equivalent tools to increase test coverage and reduce manual testing overhead
• Document detailed test results and defect reports with clear reproduction steps, severity levels, and supporting evidence for engineering teams
• Collaborate with product and engineering teams to understand requirements, clarify test scenarios, and communicate quality metrics and blockers
• Sign off on data accuracy and report integrity before production releases, ensuring stakeholder confidence in reconciliation results

SDET/QA Engineer

• Build and enhance Microsoft Copilot Studio agents
• Work with LLMs, agentic workflows, and autonomous AI actions
• Develop intelligent agents within the Microsoft ecosystem
• Integrate agents with enterprise systems using Power Automate and APIs
• Support iterative rollout, testing, optimization, and scaling of AI agents
• Design reusable agentic workflows and orchestration patterns
• Collaborate with business and technical stakeholders to translate requirements into AI-driven solutions
• Improve agent performance, prompt quality, and workflow efficiency
• Stay updated with Microsoft AI ecosystem advancements and Copilot Studio capabilities

Agentic AI Engineer

LLM Engineer

• Develop and deploy ML models and LLM-based solutions for product outcome prediction
• Build workflows where users input product details and receive predictive insights
• Enhance an existing AI agent into a decision-making/prediction system
• Work with product and assortment data for feature engineering and modeling
• Integrate LLM capabilities to improve reasoning and contextual understanding
• Continuously evaluate and improve model performance

ML, LLM Engineer

Solutions Engineer

• Design and optimize prompts that accurately interpret flagged reconciliation exceptions and generate structured, actionable resolution suggestions with confidence scoring
• Integrate LLM APIs (Anthropic Claude, OpenAI) into the exception resolution layer using Python, the Anthropic SDK, and async API patterns for production-grade reliability
• Develop human-in-the-loop workflows that balance automation with human oversight, ensuring financial accuracy and auditability at every step
• Build robust API integration code that handles edge cases, rate limiting, token management, and graceful error recovery in a financial context
• Collaborate with backend developers to embed AI outputs cleanly into the reconciliation pipeline and ensure seamless data flow into final reports
• Implement LLM evaluation and benchmarking frameworks to measure prompt effectiveness, accuracy, and consistency across diverse exception types
• Document prompt strategies and model behavior to enable knowledge sharing and continuous improvement across the engineering team
• Monitor and iterate on model performance, refining prompts and integration logic based on real-world exception data and user feedback

AI / LLM Integration Engineer

Backend Engineer

• Design and develop RESTful APIs using FastAPI
• Write complex T-SQL queries and optimize databases
• Build and maintain data models using Pydantic v2
• Author automated test suites using Pytest or similar frameworks
• Collaborate with frontend teams to define API contracts
• Participate in code reviews and architectural discussions
• Identify and resolve performance bottlenecks

Backend Developer

• Own product vision, roadmap, and execution for AI/Agentic platforms
• Drive end-to-end product lifecycle from concept to GA release
• Define and manage structured product backlog (features, enhancements, dependencies)
• Ensure release readiness (engineering, UAT, integration) with high quality standards
• Collaborate closely with Engineering, Architecture, and Design teams
• Own GTM planning including pricing, packaging, and launch readiness
• Build sales enablement assets (pitch decks, demos, solution narratives)
• Act as Product Owner for key enterprise engagements
• Lead requirement discovery, backlog creation, and delivery alignment
• Drive UAT planning, execution, and client sign-offs

Senior Product Owner – B2B AI, Agentic Platforms

Data Engineer

• Design, develop, and maintain scalable data pipelines using Databricks and Python
• Build and optimize ETL workflows for structured and unstructured data
• Work with Databricks Lakehouse architecture and implement best practices
• Manage and implement data governance using Unity Catalog
• Integrate AWS data services such as S3, IAM, VPC, and other relevant services
• Collaborate with cross-functional teams to deliver high-quality data solutions
• Build dashboards and support data visualization requirements
• Ensure data quality, reliability, and performance optimization

Senior Data Engineer, Databricks

• Design, build, and maintain Databricks data pipelines (ETL/ELT) for ingestion, transformation, and orchestration using Spark/Delta Lake/Databricks Workflows.
• Operationalize machine learning models by building inference pipelines that invoke models authored by data scientists (batch or real-time), ensuring consistency between training and inference environments.
• Ensure data reliability, quality, and observability through robust validation, monitoring, alerting, and automated recovery mechanisms.
• Collaborate closely with data scientists to productionize models, manage model deployment lifecycles, and optimize inference performance and cost.
• Implement best-practice DevOps/MLOps processes such as CI/CD for pipelines, model versioning, environment promotion, and infrastructure-as-code.
• Optimize performance and cost across compute clusters, jobs, and storage layers.
• Implement and manage the enterprise data catalog, including schema design, table ownership, lineage, governance, and documentation using Unity Catalog.
• Experience with some Databricks infrastructure.
• Experience with building BI dashboards and visualization.
• Experience with coding agents and best practices (spec-driven development, etc.)

Fullstack Data Engineer

DevOps Engineer

• Administer and maintain cloud infrastructure on Azure (VMs, Container Apps, AKS, Storage, Networking, Key Vault)
• Own CI/CD pipelines (GitHub Actions, Azure DevOps) — build, test, deploy, rollback
• Manage Docker image builds, registries (ACR), and multi-arch deployments (linux/amd64)
• Configure and harden NGINX, SSL/TLS certs (Let's Encrypt / Azure-managed), reverse proxies, and load balancers
• Monitor systems with Prometheus, Grafana, Azure Monitor, Log Analytics; set up alerts and on-call runbooks
• Automate provisioning with Terraform / Bicep / Ansible
• Manage secrets (Azure Key Vault), environment variables, and config across dev/UAT/prod
• Administer Linux servers (Ubuntu) — patching, user access, firewall (ufw/NSG), Tailscale/VPN
• Handle database ops (PostgreSQL, Supabase) — backups, restores, migrations, performance tuning
• Respond to incidents, perform RCA, and drive post-mortems
• Enforce security best practices — IAM, RBAC, least privilege, vulnerability scanning, dependency audits
• Support developers with deployment issues, env setup, and tooling

DevOps Administrator

Technical Project Manager

• Own end-to-end project delivery: planning, estimation, scheduling, and execution
• Manage RAID (Risks, Assumptions, Issues, Dependencies)
• Drive resource planning, utilization, and capacity alignment
• Ensure on-time, high-quality delivery
• Align project execution with product lifecycle stages (PDLC)
• Bridge gaps between product, engineering, and delivery teams
• Ensure alignment between roadmap, releases, and milestones
• Manage stakeholder communication and status reporting
• Lead governance forums and steering committee discussions
• Run Agile ceremonies: standups, sprint planning, reviews, retrospectives
• Track delivery metrics (velocity, burn-down, cycle time)
• Work closely with Data Science teams, AI/ML engineers, and Infrastructure and platform teams
• Ensure seamless integration across components
• Understand AI workload needs: Latency, throughput, scalability