AI/ML Engineer (Applied Machine Learning)

About the role

Voyantt Consultancy Services is looking for an AI/ML Engineer who can turn business problems into practical, measurable solutions. You will work with our engineering team to design, build, and deploy machine-learning and AI capabilities across search, recruitment, and other business applications.

Our projects include search across more than 330 million professional profiles, applicant-to-job matching, and AI-powered business workflows. Your work will involve understanding the available data, selecting appropriate techniques, validating results, and integrating solutions into production systems.

Success in this role means delivering measurable improvements while keeping systems reliable, responsive, and cost-effective. We value engineers who can investigate unfamiliar problems, question assumptions, and adapt their experience to a new business context.

Responsibilities

• Work with business stakeholders and engineers to understand requirements and translate them into clearly defined AI/ML problems.
• Analyse datasets, assess data quality, identify useful signals, and prepare training and evaluation data.
• Develop and improve models for ranking, relevance scoring, recommendations, classification, and prediction.
• Select appropriate approaches using traditional machine learning, retrieval techniques, LLMs, or a combination, supported by clear technical reasoning.
• Build baselines and experiments to measure improvements against existing systems and business objectives.
• Integrate models into applications through APIs, batch processing, and reliable data pipelines.
• Optimise solutions for latency, inference cost, infrastructure usage, and maintainability.
• Monitor deployed models, investigate errors and performance changes, and manage improvements or retraining.
• Address data leakage, biased feedback, and explainability when developing scoring and recommendation systems.
• Document decisions and communicate proposed solutions clearly to technical and non-technical colleagues.

Qualifications

• Demonstrated experience developing and deploying machine-learning solutions used in real applications.
• Strong Python and SQL skills, including experience preparing and analysing complex datasets.
• Sound understanding of feature engineering, model selection, training, validation, overfitting, and evaluation metrics.
• Practical experience with relevant libraries such as scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow.
• Experience integrating LLM APIs into applications and evaluating the quality and reliability of their outputs.
• Working knowledge of APIs, databases, version control, and cloud or container-based deployment.
• Ability to define a sensible approach when requirements are incomplete and identify what needs further investigation.
• Strong problem-solving and communication skills, including the ability to explain technical decisions and trade-offs in simple terms.
• Ability to independently carry work from investigation and experimentation through implementation and validation.

Experience with search relevance, learning-to-rank, recommendation systems, large-scale retrieval, embeddings, vector databases, hybrid search, or reranking would be an advantage. Familiarity with feedback-driven model improvement and MLOps practices—including experiment tracking, model versioning, monitoring, and retraining—is also beneficial.

How to apply

Please apply with your CV and a brief example of an AI/ML solution you personally developed. Describe the business problem, your contribution, why you chose the approach, and how you measured the result. An anonymised example is welcome.