Data & AI Consultant

PwC•Belgrade, MT

41d

About The Position

You will own end-to-end DS/ML/GenAI solutions-from problem framing and metric design to production deployment and monitoring. This is a client-facing consulting role where you'll work autonomously, mentor junior teammates, and turn complex technical problems into clear, actionable business outcomes.

Requirements

3 - 6 years of relevant experience delivering production ML/GenAI solutions
Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Math, Engineering, or equivalent experience
Strong Python and SQL; clean, modular, tested code; Git and code reviews
ML depth: feature engineering, hyperparameter tuning, cross-validation, imbalanced data handling, model selection; solid understanding of metrics
Deep learning: PyTorch or TensorFlow; building and evaluating neural models
GenAI: LLM APIs and open-source models (Llama/Mistral), embeddings; RAG with vector DBs (FAISS/Pinecone/Weaviate); prompt design and evaluation frameworks (RAGAS/TruLens); safety/guardrails (PII redaction, content filters); experience with agentic patterns/frameworks is a plus
Cloud: AWS/GCP/Azure; deploying services with Docker and CI/CD; using managed ML platforms (SageMaker, Vertex AI, Azure ML) or equivalent
Data engineering: warehousing (Snowflake/BigQuery/Redshift), ETL/ELT; orchestration (Airflow/Dagster); streaming basics (Kafka)
MLOps: MLflow or Weights & Biases; monitoring for data/model drift (Evidently or similar), logging/alerting
Security/compliance: handling PII, secrets management, governance awareness
Communication and product sense: collaborate with PMs/engineers, translate business needs into ML solutions, explain complex topics simply to non-technical audiences, and mentor juniors
Kubernetes; model serving frameworks (KServe/Seldon); feature stores (Feast)

Nice To Haves

Lakehouse tech (Delta Lake/Iceberg); Spark/Databricks
LLM serving/optimization (vLLM, TGI, TensorRT-LLM); quantization/mixed precision
Search/ranking: Elasticsearch/OpenSearch, rerankers
Advanced methods: time series, causal inference, recommendation systems, optimization
Cost optimization and IAM;
additional languages (Java/Scala/Go), bash
BI tools (Looker/Tableau)
Previous consulting experience
Publications, patents, or notable open-source contributions

Responsibilities

Scope problems with stakeholders; define success metrics and acceptance criteria
Design, build, and deploy production-grade models and data pipelines (batch and/or streaming)
Lead GenAI solutions: RAG architecture, embeddings, guardrails, and evaluation; fine-tune models (e.g., LoRA); optimize for latency and cost; design agentic workflows where applicable
Implement MLOps: experiment tracking, model versioning, CI/CD, monitoring, and alerting
Ensure data quality, security, and compliance; maintain clear documentation and communication
Mentor junior team members; perform code and experiment reviews; drive best practices