GenAI Architect

Location: Mount Laurel, NJ (Hybrid)

Duration: C2C/W2

Experience: 15+ Years

Visa: Except CPT, OPT

Technical Skills

Expertise in ELK Stack: Proficient in Elasticsearch, Logstash, and Kibana.
Visualization Experience: Strong experience with Kibana visualization tools (Lens, Maps, Graph).
Data Modeling and Knowledge of JSON and REST APIs: Familiarity with JSON data format and RESTful API principles is crucial for interacting with Elasticsearch.
Querying: Proficiency in Kibana Query Language (KQL) and Elasticsearch aggregations.
Monitoring/Observability: Background in creating operational dashboards for log analysis or metric tracking. 
Familiarity with Elasticsearch Ecosystem: Knowledge of related tools like Kibana, Logstash, and Beats enhances the engineer’s ability to deliver complete solutions.
Basic Programming Skills: Proficiency in programming languages such as Python, Java, or Go is beneficial for automation and customization tasks.

Primary: AI/GenAI- open source/commercial LLM; Python; Azure; Databricks

Secondary: Datawarehouse

Job Description:

• Develop; fine‑tune; and optimize LLMs; multimodal models; and generative AI pipelines for various business use cases.

• Build scalable AI/ML systems; including data pipelines; model training workflows; and inference services.

• Evaluate and integrate open‑source and commercial LLMs (e.g.; GPT; Llama; Claude; Mistral).

• Implement RAG (Retrieval-Augmented Generation); vector databases; embeddings; and prompt‑engineering strategies.

• Collaborate with product; engineering; and domain teams to translate business problems into AI‑powered solutions.

• Conduct experiments; benchmarking; and A/B testing to validate model performance.

• Deploy models using cloud platforms (Azure) and MLOps tools.

• Monitor; troubleshoot; and continuously improve AI systems in production.

• Strong programming skills in Python and experience with ML frameworks

• Hands‑on experience with LLMs; transformers; and generative AI architectures.

• Experience with prompt engineering; fine‑tuning; and model evaluation.

• Familiarity with MLOps tools (MLflow; Kubeflow; Docker; Kubernetes).

• Experience with agentic workflows; LangChain; LlamaIndex; or similar frameworks.

• Knowledge of multimodal AI (vision + language models).

• Hands-on data experience on Cloud Technologies on Azure; ADF; Synapse; Pyspark/Python

• Ability to understand Design; Source to target mapping (STTM) and create specifications documents

• Flexibility to operate from client office locations

• Able to mentor and guide junior resources; as needed Nice to Have

• Any relevant certifications

• Banking experience on RISK & Regulatory OR Commercial OR Credit Cards/Retail