Brooksource
Senior Technical Recruiter at Brooksource
Data Scientist
Location:
Houston
Contract Type:
6-Month Ongoing Contract (potential to hire Fulltime)
Overview
Design & deliver GenAI solutions:
Architect and implement LLM/LVM applications (text and, where applicable, vision) including prompt strategies, guardrails, evaluation metrics, cost/latency optimization, and production rollout.
Build robust RAG systems:
Stand up end‑to‑end RAG pipelines (ingestion → chunking → embedding → retrieval → synthesis), with observability, feedback loops, and AB testing for groundedness and hallucination control.
Fine‑tune foundation models:
Select, adapt, and fine‑tune open and hosted models for domain‑specific tasks using efficient techniques (LoRA/QLoRA, PEFT, parameter‑efficient adapters), and manage evaluation datasets.
Develop agentic workflows:
Implement multi‑step, tool‑using agents for real‑time, context‑aware operations; orchestrate planning, memory, and tool calling with safe‑execution policies.
ML/LLM engineering:
Build high‑quality Python code, reusable libraries, and APIs; implement CI/CD, testing, experiment tracking, and model/version governance.
Data & platforms:
Partner with data engineering to operationalize pipelines on enterprise platforms (e.g., Databricks/Snowflake) and integrate with cloud AI/ML services.
Required qualifications Mathematics & foundations
Statistics ,
Multivariate Calculus ,
Linear Algebra ,
Optimization
(you can explain choices and trade‑offs in model behavior based on these principles).
Programming
Advanced Python
(clean architecture, typing, packaging, testing; performance profiling and async where appropriate).
Deep Learning:TensorFlow
or
PyTorch
Generative AI expertise
Large Language/Vision Models (LLM/LVM):
Hands‑on with multiple providers/models (e.g.,
Gemini ,
GPT ,
Claude ,
Llama ) and their APIs.
Model fine‑tuning:
Proven experience fine‑tuning foundation models for domain tasks; evaluation design and data curation included.
Retrieval‑Augmented Generation (RAG):
Ability to design and implement robust RAG systems for
real‑time, context‑aware
applications.
Prompt engineering & agentic workflows:
Advanced prompt design (system/task/reflection patterns) and building
multi‑step AI agents .
Vector databases/search & embeddings:
Practical experience with vector indexing, similarity search, and embedding selection/management.
Software craftsmanship & platforms
Version Control:
Git (GitHub/GitLab/Bitbucket)
Databases:SQL
and
NoSQL
(e.g.,
MongoDB ,
Cassandra )
Cloud:
Hands‑on with
one or more
of
AWS ,
Azure ,
GCP , specifically AI/ML & data services (e.g.,
AWS SageMaker ,
Azure Machine Learning ,
Google Vertex AI )
Preferred qualifications
Vector stores/search:
FAISS, Milvus, Weaviate, Pinecone; hybrid (BM25 + vector) retrieval; reranking.
LLMOps/observability:
MLflow, LangSmith, OpenTelemetry, Prometheus, dashboards for cost/latency/quality; offline & online eval (e.g., RAGAS/DeepEval style).
Orchestration & data:
Airflow/Prefect; Kafka/Event Hubs; Delta/Parquet; Unity Catalog/governance.
MLOps patterns:
Canary/shadow deployments, feature flags, AB testing, blue‑green rollouts.
Domain experience:
Background in complex, datarich industries (e.g., energy, manufacturing, industrial IoT) is a plus.
Benefits
Vision insurance
Medical insurance
Referrals increase your chances of interviewing at Brooksource by 2x
Eight Eleven Group provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, national origin, age, sex, citizenship, disability, genetic information, gender, sexual orientation, gender identity, marital status, amnesty or status as a covered veteran in accordance with applicable federal, state, and local laws.
#J-18808-Ljbffr
Location:
Houston
Contract Type:
6-Month Ongoing Contract (potential to hire Fulltime)
Overview
Design & deliver GenAI solutions:
Architect and implement LLM/LVM applications (text and, where applicable, vision) including prompt strategies, guardrails, evaluation metrics, cost/latency optimization, and production rollout.
Build robust RAG systems:
Stand up end‑to‑end RAG pipelines (ingestion → chunking → embedding → retrieval → synthesis), with observability, feedback loops, and AB testing for groundedness and hallucination control.
Fine‑tune foundation models:
Select, adapt, and fine‑tune open and hosted models for domain‑specific tasks using efficient techniques (LoRA/QLoRA, PEFT, parameter‑efficient adapters), and manage evaluation datasets.
Develop agentic workflows:
Implement multi‑step, tool‑using agents for real‑time, context‑aware operations; orchestrate planning, memory, and tool calling with safe‑execution policies.
ML/LLM engineering:
Build high‑quality Python code, reusable libraries, and APIs; implement CI/CD, testing, experiment tracking, and model/version governance.
Data & platforms:
Partner with data engineering to operationalize pipelines on enterprise platforms (e.g., Databricks/Snowflake) and integrate with cloud AI/ML services.
Required qualifications Mathematics & foundations
Statistics ,
Multivariate Calculus ,
Linear Algebra ,
Optimization
(you can explain choices and trade‑offs in model behavior based on these principles).
Programming
Advanced Python
(clean architecture, typing, packaging, testing; performance profiling and async where appropriate).
Deep Learning:TensorFlow
or
PyTorch
Generative AI expertise
Large Language/Vision Models (LLM/LVM):
Hands‑on with multiple providers/models (e.g.,
Gemini ,
GPT ,
Claude ,
Llama ) and their APIs.
Model fine‑tuning:
Proven experience fine‑tuning foundation models for domain tasks; evaluation design and data curation included.
Retrieval‑Augmented Generation (RAG):
Ability to design and implement robust RAG systems for
real‑time, context‑aware
applications.
Prompt engineering & agentic workflows:
Advanced prompt design (system/task/reflection patterns) and building
multi‑step AI agents .
Vector databases/search & embeddings:
Practical experience with vector indexing, similarity search, and embedding selection/management.
Software craftsmanship & platforms
Version Control:
Git (GitHub/GitLab/Bitbucket)
Databases:SQL
and
NoSQL
(e.g.,
MongoDB ,
Cassandra )
Cloud:
Hands‑on with
one or more
of
AWS ,
Azure ,
GCP , specifically AI/ML & data services (e.g.,
AWS SageMaker ,
Azure Machine Learning ,
Google Vertex AI )
Preferred qualifications
Vector stores/search:
FAISS, Milvus, Weaviate, Pinecone; hybrid (BM25 + vector) retrieval; reranking.
LLMOps/observability:
MLflow, LangSmith, OpenTelemetry, Prometheus, dashboards for cost/latency/quality; offline & online eval (e.g., RAGAS/DeepEval style).
Orchestration & data:
Airflow/Prefect; Kafka/Event Hubs; Delta/Parquet; Unity Catalog/governance.
MLOps patterns:
Canary/shadow deployments, feature flags, AB testing, blue‑green rollouts.
Domain experience:
Background in complex, datarich industries (e.g., energy, manufacturing, industrial IoT) is a plus.
Benefits
Vision insurance
Medical insurance
Referrals increase your chances of interviewing at Brooksource by 2x
Eight Eleven Group provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, national origin, age, sex, citizenship, disability, genetic information, gender, sexual orientation, gender identity, marital status, amnesty or status as a covered veteran in accordance with applicable federal, state, and local laws.
#J-18808-Ljbffr