DS
GEN AI,
Damco Solutions Inc
šŗšø United States
On-site
Staff / Principal
5 months ago
- Machine Learning
- AI
- Python
- Language Models
- LoRA
- Incident Response
- MLOps
- RAG
- TypeScript
- LangChain
- Semantic Kernel
- CI/CD
- MLflow
- IaC
- Terraform
- Bicep
- Devops
- GitHub Actions
- Azure DevOps
- Risk Management
- ADK
- Qdrant
- FAISS
- Pinecone
- OpenTelemetry
- Prometheus
- Azure AI
- Azure OpenAI
- GCP
- Vertex AI
- AWS Bedrock
- Docker
- Kubernetes
5 months ago
Client: MSIG
Job Title
GenAI Lead Engineer
About the Role
We are looking for a GenAI Lead Engineer to design, deploy, and maintain agentic AI solutions that are safe, scalable, and businessāready. You will own endātoāend deliveryāfrom prompt and agent design to data pipelines, model deployment, observability, and rigorous validationāpartnering 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 relatedand redeploy not just create code and deploy - deep expertise in writing, understand, changing, inventing, debug and deploy AI agent using python.
What You'll Do
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.
Agent & Prompt Implementation
Help design and implement agent orchestration (single and multiāagent) 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 contentāsafety 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 fineātuning 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
approval workflows.
Collaborate with security/compliance to meet regulatory, privacy, and usageāpolicy requirements.
Minimum Qualifications
14 + 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).
Handsāon with retrievalāaugmented generation (RAG), function calling, prompt optimization, and
agent design patterns.
Experience building data pipelines (batch/stream), and managing datasets for training/fineātuning 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
MLOps fluency: model packaging, CI/CD, experiment tracking (e.g., MLflow), deployment on
cloud/container platforms.
IaC (e.g., Terraform/Bicep) and DevOps tooling (e.g., GitHub Actions/Azure DevOps); strong grasp of
observability.
Experience with multiāagent 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 openāsource 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
(OpenTelemetry/Prometheus); Experiment tracking (MLflow); Cloud AI services (e.g., Azure AI FGoundry, Azure OpenAI, GCP Vertex AI, AWS Bedrock); Containers (Docker/Kubernetes).
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.
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Regards,
K P ā Team Lead Recruitments
Direct: +1 609 775 9549 Ext: 137 | Email : kpunna@wscs.ai
Wall Street Consulting Services, LLC | 100 Overlook Center,2nd Floor, Princeton, NJ 08540 | www.wscs.ai
GEN AI, Ā· Damco Solutions Inc