Galent · 1 day ago
Artificial Intelligence Engineer
New York, United States
Full-time
Onsite
Mid, Senior Level
$130K/yr - $140K/yr
4+ years expGalent is a financial organization seeking a Senior AI Engineer/ AI Evangelist to bridge the gap between AI technologies and business needs. The role involves building AI-powered solutions, translating AI concepts for stakeholders, and leading workshops to promote AI literacy within the organization.
Computer Software
Hiring Manager
Prashanth Rajendran
Responsibilities
Build and demonstrate AI-powered solutions for financial applications preferably in investment banking, Trading or insurance environments
Translate complex AI concepts into actionable business value for both technical and non-technical stakeholders, simplifying information for internal teams and executive leadership
Lead workshops, seminars, and training sessions for teams across the organization, promoting AI literacy and upskilling staff in banking, investment, or insurance environments
Ability and /or experience in authoring technical blogs, white papers, and internal documentation that explain the impact and possibilities of AI in the financial domain
Qualification
Required
Bachelor's or master's degree in computer science, Data Science, Finance, or related field
Experience in one or many of the high-level programming languages like C++, Java, C#
Good understanding of Typescript, Node.js and other JS framework for UI development
Strong hands-on experience with Python, SQL, and AI/ML frameworks (e.g., TensorFlow, PyTorch) as applied to financial data and workflows
At least 4+ years working in AI roles within finance, fintech, or technical consulting, preferably with exposure to regulatory environments
Deep knowledge of AI ethics, compliance, Guardrails, data privacy, and compliance trends relevant to the financial sector
Excellent communication, stakeholder engagement, and technical storytelling abilities
Demonstrated ability to manage multiple priorities and projects while maintaining strategic alignment and rigorous attention to detail
Advanced Python expertise, plus experience with other major backend languages (e.g., Java, C++, Go) and modern AI/ML toolkits
Demonstrated proficiency in designing, validating, and launching code-generation systems and agentic workflows, strong familiarity with prompt engineering and AI model deployment
Track record of hands-on technical leadership within agile teams, overseeing both human and AI-generated codebases and ensuring auditability, explainability, and compliance at scale
Expertise in code review, automated testing, and documentation standards for mixed human/AI development environments
Prompt Engineering: Crafting structured prompts to drive deterministic and reproducible outputs from LLMs, using techniques like chain-of-thought and few-shot prompting
Context Engineering: Dynamically injecting relevant external data into prompts; designing and managing context windows, handling retrieval noise and context collapse in long-context
Fine-Tuning & Model Adaptation: Using methods like LoRA/QLoRA for domain adaptation, managing data curation pipelines, and monitoring overfitting versus generalization—especially in high-stakes environments
Retrieval-Augmented Generation (RAG): Building LLM workflows with external knowledge integration, engineering embeddings and retrieval pipelines for high recall and precision
Agentic Design: Orchestrating LLM-driven agents capable of multi-step reasoning, tool use, and autonomous state management—including fallback strategies for error
Production Deployment: Packaging models and agentic systems as scalable APIs, with robust pipelines for latency, concurrency, and failure isolation, including container orchestration or serverless deployment
LLM Optimization: Applying quantization, pruning, and distillation to optimize performance and cost; benchmarking for speed, accuracy, and hardware utilization
Observability & Monitoring: Implementing logging, tracing, dashboards, and alignment monitoring for prompts, responses, and agent behaviors
Core SDLC AI Integration: Using generative AI for requirement refinement, technical design blueprinting, architecture review, API and schema auto-generation, and cross-functional artifact production
Security & Compliance: Building guardrails to enforce data privacy, compliance with regulations, and responsible use of LLMs, particularly in sensitive or regulated environments
Modern Deep Learning: Mastery of frameworks including TensorFlow, PyTorch, and HuggingFace Transformers, with proven expertise in transformers, CNNs, RNNs, and attention mechanisms for custom and state-of-the-art model
GitHub Copilot: Mainstream AI-powered code generation and completion for major languages, widely integrated into enterprise SDLC
ChatGPT/GPT-4/Vision: Prompt-driven code assistance, architecture brainstorming, documentation generation, and natural language requirement mapping
SonarQube: AI-powered static code analysis and vulnerability detection for code security and quality assurance across SDLC
Jira (with AI plugins): AI-enhanced project management, backlog refinement, and sprint planning—crucial for orchestrating product delivery at scale
Claude Code: Multi-step code generation and agentic orchestration, especially suitable for agent-based SDLC
Datadog and Dynatrace: Proactive AI in monitoring, predictive analytics, and incident response for production reliability and observability
RAG frameworks like Langchain, Langraph, LlamaIndex, Graph RAG
Graph database -RD4j, Neo4j and timeseries database
Embeddings & Vector Databases: Understanding embeddings, vector search, vector DB platforms (FAISS, Pinecone, Chroma, Weaviate), and semantic retrieval
Observability & Evaluation: Setting up logging, debugging, and automated quality evaluation for RAG applications (e.g., with TruLens, Streamlit dashboards)
Containerization/DevOps: Packaging with Docker or similar, using cloud/AWS/Azure integrations for scalable deployments
Preferred
Sourcegraph Cody: LLM-powered search, code context awareness, and auto-completion over vast enterprise repositories
Cursor/Codex/Windsurf: AI-native development workflow management; designed for large-scale coding and agentic workflows
Amazon Q (AWS): AI-driven code, architecture recommendation, and AWS-native code and cloud resource management
Synapt SDLC Squad: Multi-agent generative AI platform for end-to-end SDLC automation, code review, and compliance
Bitbucket/GitLabs (with AI modules): AI-assistance in code review, merge requests, release management, and security
Figma (AI for Design/Prototyping): AI documentation, prototyping, and developer handoff for frontend/UI
Lightweight Architecture ADLs: Using architecture definition languages (ADLs) to leverage LLMs for generating structural constraints, fitness functions, and architecture governance
Automated UI/UX Prototyping: Leveraging generative tools (e.g., Claude, RunwayML) for rapid wireframe and design generation from requirements or stakeholder
End-to-End SDLC Automation: Experience with multi-agent platforms and orchestration tools for automating requirement gathering, code review, compliance, and release management
Cost-Efficiency & Sustainability: Implementing sustainable AI practices, carbon-aware scheduling, and model lifecycle management for production
Emergent LLM Platforms: Experience integrating and orchestrating new LLMs, open-source agents, vector DBs, and hybrid architectures beyond mainstream offerings
Ethical AI & Governance: Leading the definition of internal standards and policies for responsible, bias-safe LLM operation and agentic workflows
Domain-Specific Knowledge: Deep knowledge of applying LLMs and generative AI in specialized contexts, such as finance, Banking, or other regulated domains
Company
Galent
Galent is an AI-native digital engineering firm at the forefront of the AI revolution, dedicated to delivering unified, enterprise-ready AI solutions that transform businesses and industries.
501-1000 employees
Funding
Current Stage
Late StageCompany data provided by crunchbase