AI/ML Engineer

Andersen·Баку·14 ч назад

AI/ML Engineer

🏢 ОфисMiddleПолная занятость

Зарплата не указана

Чем предстоит заниматься

Owning the learning-to-rank stack end to end, including training data, features, model development, offline evaluation, production serving, and regression analysis

Improving the LambdaMART reranker and determining when gradient boosted trees are the appropriate solution

Designing and maintaining the offline evaluation framework, including NDCG@K, Precision@K, negative sampling strategies, dataset construction, and analysis of the gap between offline metrics and production behavior

Designing hybrid retrieval systems combining BM25 and dense vector search

Owning the recall budget feeding the reranker

Building feature sets for structured candidate-to-job matching, including skills coverage, seniority alignment, location and mobility signals, recency, and evidence strength

Handling sparse and partially populated profiles as a core design requirement, including calibration and monotonic constraints where needed

Diagnosing ranking quality regressions in production and identifying whether the root cause is related to features, labels, indexes, or models

Ensuring ranking decisions are explainable enough to satisfy Annex III obligations and support recruiter-facing explanations

Defining technical direction for matching systems

Reviewing contributions from other engineers working on the matching stack

Mentoring engineers on ranking fundamentals

Transforming interview transcripts and extracted claims into ranking features and supervision signals

Designing LLM-as-judge and weak supervision approaches for pairwise labels while managing label noise and consistency

Working on constrained extraction and span grounding to ensure claims are traceable to candidate statements

Contributing to interview integrity signals and confidence representation downstream

Using LLMs as components within larger systems rather than as core ranking models

Hands-on Machine Learning experience for 5+ years, with a focus on search, ranking, recommender systems, or relevance systems

Eperience building and operating production-grade ranking and retrieval systems for 3+ years

Strong practical experience with Learning-to-Rank, including gradient-boosted tree models such as LightGBM or XGBoost

Ability to explain and apply pairwise and listwise ranking objectives, selecting the appropriate approach based on real-world trade-offs

Strong expertise in ranking evaluation, including the development and maintenance of offline evaluation pipelines

Strong experimental rigor, including designing meaningful baselines; running ablation studies; distinguishing genuine improvements from noise, particularly with small or noisy evaluation datasets; reading and reproducing relevant research papers

Practical experience working with sparse, incomplete, or noisy data, including appropriate feature-engineering techniques

Experience generating scalable training signals without relying on manual annotation

Strong Python and ML engineering skills, including hands-on experience with scikit-learn

Ability to communicate complex technical topics clearly to both technical and non-technical stakeholders, including recruiters and compliance teams

Ownership of the full ranking funnel, from retrieval and recall through reranking and evaluation, including diagnosing ranking regressions and their root causes

Ability to approach data quality as a modeling challenge and balance relevance, latency, inference cost, and model complexity

Experience delivering production ML solutions independently, without relying on large infrastructure teams

Background in search, ads ranking, recommender systems, or marketplace ranking

Experience designing ranking benchmarks or evaluation metrics

Applied research experience through relevant publications or industrial research that resulted in production solutions

Level of English – from Upper-Intermediate and above

HRTech experience

Recruitment technology experience

Job marketplace or talent-matching experience

Vespa, Elasticsearch, or OpenSearch expertise, including index design and query tuning

Production experience with vector search technologies such as Vespa, FAISS, Qdrant, and Pinecone

Experience with model ensembling, including stacking and blending

Experience building regulated or auditable ML systems

LLMOps and experiment tracking experience

Experience with LangFuse

Experience in teamwork with leaders in FinTech, Healthcare, Retail, Telecom, and others. Andersen cooperates with such businesses as Samsung, Siemens, Johnson & Johnson, BNP Paribas, Ryanair, Mercedes, TUI, Verivox, Allianz, T-Systems, etc

The opportunity to change the project and/or develop expertise in an interesting business domain

Job conditions – you can work both fully remotely and from the office or can choose a hybrid variant

Guarantee of professional, financial, and career growth! The company has introduced systems of mentoring and adaptation for each new employee

The opportunity to earn up to an additional 1,000 EUR per month, depending on the level of expertise, which will be included in the annual bonus, by participating in the company's activities

Access to the corporate training portal, where the entire knowledge base of the company is collected and which is constantly updated

Bright corporate life (parties / pizza days / PlayStation / fruits / coffee / snacks / movies)

Certification compensation (AWS, PMP, etc)

Referral program

Private health insurance and sports compensation, depending on the type of employment

Дополнительно

Andersen is hiring an AI/ML Engineer for a project developing an AI-native talent intelligence platform and improving candidate matching and recruitment efficiency

The customer is an international company focused on delivering innovative digital solutions and technology-driven services. The organization supports businesses in improving operational efficiency, streamlining processes, and accelerating digital transformation initiatives. By leveraging modern technologies and industry best practices, the company develops scalable and secure solutions tailored to evolving business needs. The customer operates in a dynamic environment, emphasizing innovation, collaboration, and the continuous enhancement of products and services to create long-term value for its clients

The project is focused on developing and enhancing an AI-native talent intelligence platform for the European market. It provides intelligent candidate-job matching through advanced CV analysis, skill normalization, hybrid search, and ranking capabilities, aiming to improve the accuracy and efficiency of talent discovery and recruitment