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.

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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 improves

AI codebases

for reliability and performance.Deploys and manages

machine learning platforms

for predictive model training.Implements and maintains

generative AI systems

with frameworks like

SGLang, TGI, vLLM .Optimises

natural language processing (NLP)

tasks (summarisation, sentiment analysis, keyword extraction, categorisation).Develops and maintains

retrieval-augmented generation (RAG)

systems, indexing, embedding, and reranking.Builds

evaluation frameworks

assessing AI output faithfulness, relevance, and truthfulness.Enhances solutions with a focus on

efficient compute usage

for environmental sustainability, cost-effectiveness, and performance.Supports and contributes to improvements in

AWS infrastructure , considering architecture optimisation, scalability, and robustness in collaboration with the DevOps team.Integrates AI solutions across

backend infrastructure and front-end interfaces .Investigates and resolves complex technical issues with proactive debugging.Communicates effectively to stakeholders of varying

technical expertise .Mentors junior team members, promoting best practices and skills development.

Essential Skills:

Cloud & Infrastructure:Foundational

understanding of AWS

or other cloud platforms, with an awareness of deploying and managing multi-tenant infrastructure.Ability to contribute ideas for

architectural optimisation , improving scalability and robustness with support from the DevOps team.

Programming:Excellent programming and debugging skills in

Python , including libraries like

pandas ,

FastAPI , and

Pydantic .Experience with version control systems, particularly

Git .Ability to maintain and enhance

APIs .Understanding of

database management

using

PostgreSQL , including database models and entity diagrams.

ML/AI:Strong knowledge and practical experience in machine learning

model training ,

evaluation , and

deployment .Experience with

AutoML

libraries (e.g., AutoKeras).Solid experience with natural language processing ( NLP ) tasks and retrieval-augmented generation ( RAG ) systems.Expertise in

embedding

models,

indexing

techniques, and

reranking

methods.Familiarity with frameworks and libraries like HuggingFace and LangChain.

Deployment:Proficiency with

deploying large language models

(LLMs) using frameworks like SGLang, TGI, or vLLM.Proficiency in

Linux

and command-line interface for system administration and automation.Basic foundation in AWS or other cloud service providers to deploy

multi-tenant infrastructures , managing and segregating user access control.Understanding of

Kubernetes

and containerised applications orchestration, including inter-service communication.

Development Methodologies:Familiarity with

Agile development

processes including daily stand-ups, weekly catch-ups, retrospectives, and hybrid approaches.

Desirable Skills:

Familiarity with

agentic AI frameworks

such as PydanticAI or smolagents.Experience with

fine-tuning

large language models (LLMs).Interest or experience with

MCP ,

A2A , or

AutoGen .Keeps up to date with the

latest trends

in 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.