GenAI Developer

Wall Street Consulting Services LLC; New Jersey; On-site; USD 150,000 - 210,000; Full time

Job summary

Wall Street Consulting Services LLC in the United States seeks a GenAI engineer to design, deploy, and maintain agentic AI solutions that are safe, scalable, and business-ready. You will own end-to-end delivery from prompt design to data pipelines, model deployment, observability, and rigorous validation.

The role requires strong Python coding skills, experience with LLM/GenAI in production, and hands-on work with tools like LangChain and Semantic Kernel, vector stores, and modern CI/CD

Qualifications

• 4–8+ years in software/ML engineering, with 2+ years building LLM/SLM/GenAI solutions in production.
• Proficiency in Python (and/or TypeScript) and modern AI orchestration frameworks.
• Hands-on with retrieval-augmented generation (RAG), function calling, prompt optimization, and agent design patterns.
• Experience building data pipelines and managing datasets for training/fine‑tuning and evaluation.
• Practical understanding of AI guardrails: content filtering, safety policies, redaction, rate limiting.

Responsibilities

• Design, deploy, and maintain agentic AI solutions in production.
• Develop data pipelines for fine‑tuning and retrieval (RAG).
• Implement observability, monitoring, and incident response for AI services.
• Collaborate with product, architecture, security, and QA to ensure safe, scalable deployments.
• Create prompts, tools, and orchestration patterns for multi‑agent systems.

Skills

Python

TypeScript

LLM/GenAI development

LangChain

Semantic Kernel

RAG

Data pipelines

Observability

Multi-agent orchestration

Tools

LangChain

Semantic Kernel

Qdrant

FAISS

Pinecone

GitHub Actions

Azure DevOps

Terraform

Bicep

Job description

We are looking for a GenAI to design, deploy, and maintain agentic AI solutions that are safe, scalable, and business‑ready. You will own end‑to‑end delivery—from prompt and agent design to data pipelines, model deployment, observability, and rigorous validation—partnering with product, architecture, security, and QA to ship AI features that perform reliably in production.

*Update - Python Coding Experience is required. Someone who is well versed with understanding and coding Python, make changes add/change rules specially insurance related and redeploy not just create code and deploy - deep expertise in writing, understand, changing, inventing, debug and deploy AI agent using python.

SLM Design and Fine Tunning

• Collect, clean, and preprocess domain-specific datasets for SLM training and fine‑tuning.
• Ensure data quality, diversity, and compliance with privacy and security standards.
• Fine-tune small language models on curated datasets using techniques like LoRA, adapters, or
• parameter‑efficient tuning.
• Optimize hyperparameters for performance, latency, and resource efficiency.
• Help design and implement agent orchestration (single and multi‑agent) and function/tool use strategies.
• Craft, version, and optimize prompts and system instructions for accuracy, coherence, and domain alignment.
• Integrate external tools/APIs and establish content‑safety guardrails (e.g., policy enforcement, PII redaction, jailbreak prevention).

Implementation, Testing & Maintenance

• Build resilient agent workflows and services; harden reliability with retries, fallbacks, circuit

breakers.

• Develop automated tests for prompts, tools, and agent behaviors; maintain regression suites and golden datasets.
• Operate AI services in production: performance tuning, cost optimization, incident response, and iterative improvement.

Data & MLOps

• Design and manage data pipelines for fine‑tuning and retrieval (RAG), including cleansing,
• labeling, and governance.
• Monitor drift, quality, latency, and safety signals; implement model/agent observability and alerting.

Quality Assurance & Risk

• Run structured evaluations of agent outputs (functional, coherence, safety, bias); track

precision/recall and hallucination rates.

• Perform risk assessments for agent behaviors and tool actions; document mitigations and
• Collaborate with security/compliance to meet regulatory, privacy, and usage‑policy requirements.

Minimum Qualifications

• 4–8+ years in software/ML engineering, with 2+ years building LLM/SLM/GenAI solutions in
• production.
• Proficiency in Python (and/or TypeScript) and modern AI orchestration frameworks (e.g., Microsoft
• Agent Framework, Google Agent Development Kit, LangChain, Semantic Kernel).
• Hands‑on with retrieval‑augmented generation (RAG), function calling, prompt optimization, and agent design patterns.
• Experience building data pipelines (batch/stream), and managing datasets for training/fine‑tuning and evaluation.
• Practical understanding of AI guardrails: content filtering, safety policies, redaction, rate limiting, and misuse prevention.
• Strong willingness to learn advanced agent orchestration and MLOps practices.

Preferred Qualifications

cloud/container platforms.

• IaC (e.g., Terraform/Bicep) and DevOps tooling (e.g., GitHub Actions/Azure DevOps); strong grasp of observability.
• Experience with multi‑agent systems, toolformer patterns, and complex orchestration graphs.
• Knowledge of vector databases and retrieval systems; evaluation frameworks (e.g., Ragas, DeepEval) and custom metrics.
• Familiarity with privacy, compliance, and model risk management practices for AI.
• Background in tuning open‑source and hosted models; comfort with hybrid cloud environments.

Tools & Technologies

• Python; TypeScript; MAF/Google ADK/LangChain/Semantic Kernel; Vector DBs and frameworks (e.g.,Qdrant/FAISS/Pinecone); CI/CD (GitHub Actions/Azure DevOps); IaC (Terraform/Bicep); Observability

Working Model

• Partner with Product, Architecture, Security, and QA to plan, design, and ship safe AI features.
• Contribute to internal prompt standards, evaluation datasets, and reuseable components.

Document designs, decisions, and risks; mentor peers and champion responsible AI practices.