Senior Machine Learning Engineer

At Netcall, we deliver powerful low-code, robotic process automation (RPA), and contact centre solutions that enable organisations to transform and streamline their processes. Central to our innovative platform, Liberty, is our dedicated Liberty AI Team, which integrates sophisticated, robust, and thoroughly tested AI capabilities across various products including low-code development, process mapping, automation, and customer service solutions. Our AI team collaborates closely with diverse product teams, ensuring seamless integration and maximising value through intelligent, user-focused enhancements.

Purpose of Role:

To maintain, enhance, and deploy AI services and codebases, ensuring high-quality integration and performance of machine learning models and generative AI solutions within the Netcall Liberty platform.

Key Responsibilities:

• Maintains and improvesAI codebasesfor reliability and performance.
• Deploys and managesmachine learning platformsfor predictive model training.
• Implements and maintainsgenerative AI systemswith frameworks likeSGLang, TGI, vLLM.
• Optimisesnatural language processing (NLP)tasks (summarisation, sentiment analysis, keyword extraction, categorisation).
• Develops and maintainsretrieval-augmented generation (RAG)systems, indexing, embedding, and reranking.
• Buildsevaluation frameworksassessing AI output faithfulness, relevance, and truthfulness.
• Enhances solutions with a focus onefficient compute usagefor environmental sustainability, cost-effectiveness, and performance.
• Supports and contributes to improvements inAWS infrastructure, considering architecture optimisation, scalability, and robustness in collaboration with the DevOps team.
• Integrates AI solutions acrossbackend infrastructure and front-end interfaces.
• Investigates and resolves complex technical issues with proactive debugging.
• Communicates effectively to stakeholders of varyingtechnical expertise.
• Mentors junior team members, promoting best practices and skills development.

Essential Skills:

Cloud & Infrastructure:

• Foundationalunderstanding of AWSor other cloud platforms, with an awareness of deploying and managing multi-tenant infrastructure.
• Ability to contribute ideas forarchitectural optimisation, improving scalability and robustness with support from the DevOps team.

Programming:

• Excellent programming and debugging skills inPython, including libraries likepandas,FastAPI, andPydantic.
• Experience with version control systems, particularlyGit.
• Ability to maintain and enhanceAPIs.
• Understanding ofdatabase managementusingPostgreSQL, including database models and entity diagrams.

ML/AI:

• Strong knowledge and practical experience in machine learningmodel training,evaluation, anddeployment.
• Experience withAutoMLlibraries (e.g., AutoKeras).
• Solid experience with natural language processing (NLP) tasks and retrieval-augmented generation (RAG) systems.
• Expertise inembeddingmodels,indexingtechniques, andrerankingmethods.
• Familiarity with frameworks and libraries like HuggingFace and LangChain.

Deployment:

• Proficiency withdeploying large language models(LLMs) using frameworks like SGLang, TGI, or vLLM.
• Proficiency inLinuxand command-line interface for system administration and automation.
• Basic foundation in AWS or other cloud service providers to deploymulti-tenant infrastructures, managing and segregating user access control.
• Understanding ofKubernetesand containerised applications orchestration, including inter-service communication.

Development Methodologies:

• Familiarity withAgile developmentprocesses including daily stand-ups, weekly catch-ups, retrospectives, and hybrid approaches.

Desirable Skills:

• Familiarity withagentic AI frameworkssuch as PydanticAI or smolagents.
• Experience withfine-tuninglarge language models (LLMs).
• Interest or experience withMCP,A2A, orAutoGen.
• Keeps up to date with thelatest trendsin RAG solutions, agentic AI, and generative AI implementations.

Behavioural Competencies:

• Accountability:Takes ownership and responsibility for tasks and outcomes.
• Proactiveness:Anticipates needs, takes initiative, and seeks continuous improvement.
• Agility:Demonstrates flexibility and adaptability in a dynamic environment.
• Customer Focus:Prioritises user experience and customer satisfaction.
• Collaboration:Effectively works with and mentors colleagues, promoting teamwork and shared success.
• Communication:Clearly articulates complex technical information to diverse stakeholders.