AI Engineer

• Discover Data Science and Artificial Intelligence methods, tools, and use cases through innovative projects for clients or internal research and development activities
• Work under the guidance of an expert consultant to design and implement data- and AI-driven solutions
• Study and assess data sources
• Prepare, clean, and enrich datasets
• Perform statistical analyses and create dashboards
• Develop and train Machine Learning models
• Experiment with generative AI solutions and intelligent agents
• Evaluate performance and optimize models
• Prepare technical documentation and present results
• Contribute to demonstrators and innovation projects

Data/AI Developer – Internship

• Develop, implement, and maintain high-quality conversational AI applications on the Omilia Cloud Platform
• Own end-to-end development and unit testing of conversational features and miniApps
• Implement business logic and conversational flows defined in Detailed Design Specifications
• Create, upload, and basic-tune NLU models and resources
• Troubleshoot and resolve defects identified during QA, UAT, or production hypercare
• Deploy and promote applications across OCP Dev, UAT, and Prod environments
• Write maintainable code for conversational dialogs integrating backend web services and CCaaS platforms
• Prepare and upload NLU training data and manage intent selection policies
• Analyse ELK/Kibana logs and resolve runtime errors
• Maintain technical documentation and contribute to master prompt lists and implementation steps
• Participate in daily stand-ups and sprint planning, estimate tasks, and surface technical risks
• Work within a squad under a Team Lead or Solution Delivery Manager

Conversational AI Application Developer, English and French Speaker

• Define technical strategy and architecture for Python-based AI backend platforms using FastAPI
• Architect scalable, reliable, and secure AWS systems using Lambda and EKS
• Drive technical decisions affecting organization-wide engineering practices and platform capabilities
• Design and champion AI system observability using LangChain, Langfuse, and litellm
• Establish standards for monitoring, logging, observability, and operational excellence
• Lead initiatives to optimize performance, security, and scalability of large-scale AI applications
• Mentor and technically guide engineers through code reviews, architecture reviews, and technical leadership
• Identify technical debt, performance bottlenecks, and architectural limitations and drive long-term solutions
• Collaborate with engineering leadership, product, and stakeholders to align technical strategy with business objectives
• Influence technology choices, design patterns, and best practices across the AI Software & Operations department
• Represent the team in organization-wide technical discussions and drive adoption of innovative solutions

Staff Software Engineer – AI Backend Services

• Design, develop, and evolve strategic business applications
• Contribute to architectural decisions and key technology choices
• Ensure the robustness, performance, and maintainability of systems
• Lead complex technical projects from end to end
• Identify technical risks and propose appropriate solutions
• Support the modernization and evolution of existing platforms
• Translate complex business challenges into robust, scalable technical solutions
• Identify and assess opportunities offered by AI for products and operations
• Design and integrate AI solutions into business workflows and internal tools
• Use AI-assisted development tools such as Cursor, Claude, and GitHub Copilot
• Experiment with and implement AI-based workflows, agents, plugins, or internal tools
• Explore LLM, RAG, MCP, and AI agent technologies
• Define best practices for using AI
• Help shape the AI strategy in collaboration with the CTO
• Support teams in adopting new tools and practices
• Work closely with the CTO on technical direction
• Contribute to the continuous improvement of engineering practices
• Collaborate with product and business teams to build tailored solutions

Senior Software Engineer – AI

• Contribute to the design and integration of AI features into Ciril GROUP’s products
• Provide technical leadership for AI projects
• Support the professional development of junior colleagues
• Promote the adoption of AI best practices across teams
• Participate in code reviews, CI/CD, automated regression testing, and code analysis
• Write technical documentation
• Ensure the quality and reproducibility of development work
• Collaborate with technical, product marketing, and DevOps teams

Senior AI Developer, R&D

• Work across the entire AI project lifecycle: ideation, experimentation, scaling, and industrialization
• Define and promote AI technical standards and best practices relating to code quality, performance, security, maintainability, API integration, and cloud deployment
• Provide AI technical expertise and challenge architecture and modeling decisions
• Select and oversee the use of commercial, open-source, and cloud-based LLMs
• Guide the implementation of effective RAG techniques
• Ensure the performance, compliance, security, adoption, and business operation of AI models
• Ensure the scalability, robustness, and interoperability of models with existing systems
• Optimize performance and resource consumption
• Contribute to defining environmental KPIs related to the use of AI models, including energy efficiency, carbon footprint, and GPU resource utilization
• Analyze KPI trends and propose optimization initiatives to improve energy efficiency and reduce environmental impact
• Maintain continuous awareness of advances in AI and propose innovative technical improvements
• Support cross-functional teams of Developers, Data Scientists, and Architects in applying development and industrialization best practices
• Work with the software vendor and operations provider to deploy and integrate AI models into the client’s cloud and on-premise infrastructures
• Propose and oversee technical and methodological improvements to optimize the integration and deployment of AI models

AI Tech Lead

• Design, build, and optimize the agentic stack powering conversational and autonomous agents, with a focus on reliability, scalability, and low latency in production
• Architect and maintain end-to-end LLM features: model orchestration, routing, monitoring, logging, and evaluation
• Develop and improve ML infrastructure, including observability, load balancing, scaling patterns, and deployment workflows
• Build robust datasets and evaluation pipelines for deterministic systems (ASR) and non-deterministic systems (LLMs and agents)
• Collaborate with Software Engineers to build scalable, observable, and automated infrastructure around ML services
• Work with Product Managers to assess feasibility, refine specifications, and reliably deliver ML features
• Contribute to the technical strategy, including model and tooling selection, architectural decisions, and the agentic platform roadmap
• Stay up to date with research, frameworks, and open-source developments in LLMs, agentic systems, and evaluation methodologies

• Build, improve, and scale features for Lini, DiliTrust's proprietary AI engine
• Contribute at the intersection of AI, product, and engineering
• Own features end-to-end from technical design through deployment
• Write specifications with executable acceptance criteria and non-functional requirements
• Define data classification, authorization matrices, volumetry assumptions, and latency and throughput budgets
• Freeze API schemas, types, module boundaries, and invariants before implementation fan-out
• Pilot coding agents with bounded contexts, defined artifacts, stop conditions, budgets, and traceability
• Maintain separation between code producers and verifiers
• Independently verify peers' work using adversarial reviews against specifications
• Build property tests, contract tests, query-count and allocation budgets, execution-plan checks, policy-as-code, and backward-compatibility proofs
• Sample code by risk zone to calibrate generator and verification-gate defect rates
• Keep work-in-progress low through capped PR sizes, thin vertical slices, short-lived branches, feature flags, and CI-first workflows
• Design for blast radius using reversibility, progressive delivery, structured logs, traces, metrics, and instrumentation

Senior Product Engineer – AI Native

• Work directly with business teams to understand their needs, define scope and impact, and prioritize initiatives
• Build internal tools end to end: scoping, development, production deployment, and iteration
• Evolve the shared AI platform, MCP servers, integrations, and cross-functional tooling
• Make technical decisions regarding infrastructure, technology stack, and architecture
• Mentor the junior AI Engineer and share best practices
• Identify processes where AI can deliver gains and build the solutions
• Build a system to analyze customer feedback logs and generate recommendations for Product Managers
• Deploy MCP servers connected to employees’ everyday tools at scale
• Work directly with the Sales, Customer Success, Finance, Product, Marketing, Data, and C-level teams

AI Software Engineer – AI Lab

• Train, fine-tune, and evaluate proprietary language models, including small models running on-device
• Curate and maintain datasets, golden sets, and evaluation harnesses
• Ship model updates to production and measure their impact
• Own the health of production AI features across quality, cost, and latency
• Detect silent regressions and debug AI incidents end to end
• Maintain models, prompts, and pipelines as tooling and APIs evolve
• Partner with product managers and engineers on new AI use cases
• Build rapid, evidence-based prototypes and provide evaluation, cost, and latency data
• Explain AI trade-offs to non-technical colleagues
• Document work and mentor junior engineers and interns on AI topics
• Work alongside the CTO and engineering squads, owning the AI workstream for a product area