Job Description
About SpotDraft.
SpotDraft is on a mission to help legal and business teams move faster, together. Our AI-powered contracting platform is redefining how companies manage contracts, and our story deserves to be told in creative, human, and memorable ways.
Job Summary
SpotDraft is revolutionizing legal operations with AI-powered tools that help legal teams work faster and smarter. Our flagship products leverage cutting-edge LLM technology to automate routine legal work and accelerate contract review.
Sidebar is an AI-powered team of legal assistants that handles routine legal work—from contract analysis and legal research to compliance tracking and drafting SpotDraft. It tackles everything beyond contracts, including policy questions, regulatory compliance, and strategic advice SpotDraft, learning from your organization's knowledge to become a specialized legal co-pilot.
VerifAI is an AI contract review tool that works as a Microsoft Word add-in, helping legal teams review contracts up to 70% faster with SpotDraft. It uses generative AI to check contracts against personal or organizational guidelines and answer open-ended questions SpotDraft, automatically flagging deviations and suggesting improvements.
The Role
As an Applied AI Engineer, you'll architect and deploy the production AI systems powering these products. You'll build distributed, fault-tolerant services that process millions of legal documents, designing multi-agent architectures and optimizing LLM performance for accuracy, speed, and cost efficiency in our mission-critical SaaS environment.
What You’ll Do
Build Production AI Features
Design and implement end-to-end AI features using transformer-based architectures, fine-tuned models, and prompt engineering at scale for legal document workflows (summarization, clause extraction, document comparison, intelligent drafting)
Build stateful agentic systems using ReAct/Tree-of-Thoughts frameworks with memory persistence, tool orchestration (function calling), multi-turn reasoning capabilities, and chain-of-thought prompting handling 10K+ concurrent sessions
Architect Scalable RAG & Context Systems
Design semantic chunking, hybrid search (BM25 + dense embeddings), reranking models, query decomposition, and HyDE (Hypothetical Document Embeddings) for large legal documents
Build retrieval-grounded generation pipelines with dynamic context window allocation and efficient vector database queries for improved precision/recall
Optimize Infrastructure & Performance
Design distributed systems with horizontal scaling, load balancing, and circuit breakers for LLM inference services processing millions of requests/day
Implement inference optimizations: request batching, semantic caching, KV-cache optimization, quantization (INT8/FP16), and model serving frameworks (vLLM, TensorRT-LLM, TGI)
Build low-latency API gateways with rate limiting, retry logic, and fallback mechanisms across multiple LLM providers (OpenAI, Anthropic, Google Gemini)
Ensure Quality & Reliability
Build multi-layered hallucination mitigation with fact-checking agents, confidence calibration, and citation validation
Design evaluation harnesses with automated metrics (BLEU, ROUGE, BERTScore, semantic similarity), LLM-as-judge pipelines, and human-in-the-loop labeling workflows
Create experimentation platforms for A/B testing prompt variations, model comparisons, and configuration changes with statistical rigor
MLOps & Observability
Build MLOps pipelines with automated training, evaluation, and deployment using Kubernetes, Argo Workflows, and feature stores
Implement blue-green deployments, shadow traffic testing, and automated rollback mechanisms with SLO-based monitoring
Create monitoring stacks with distributed tracing, anomaly detection, RLHF feedback loops, and dashboards tracking latency (p50/p95/p99), token consumption, error rates, and cost per request
What we’re looking for
Must Have
Experience : 6+ years in AI/ML engineering with a proven track record deploying LLM-based systems at scale.
LLM Expertise: Deep hands-on experience with GPT-4, Claude, Gemini, or Llama models via API integration, fine-tuning (LoRA, QLoRA), and prompt optimization techniques.
Production AI Systems: Shipped production AI features (chatbots, RAG systems, document intelligence) with measurable impact on latency, accuracy, and cost metrics.
Technical Depth:
Expert-level Python with strong software engineering fundamentals (design patterns, testing, profiling).
Deep understanding of transformer architectures, attention mechanisms, and embedding models.
Proficiency with vector databases (Pinecone, Weaviate, Qdrant, pgvector) and semantic search.
Experience with distributed systems, microservices architecture, and async programming (asyncio, FastAPI, gRPC).
Strong knowledge of data structures, algorithms, and computational complexity for optimization.
MLOps & Infrastructure: Hands-on experience with containerization (Docker, Kubernetes), CI/CD pipelines, and cloud platforms (AWS SageMaker, GCP Vertex AI, Azure OpenAI).
Performance Optimization: Demonstrated ability to optimize inference latency (p95 < 2s), and scale systems to handle traffic spikes.
Evaluation & Metrics: Experience designing evaluation frameworks, building synthetic test datasets, and implementing A/B testing for ML systems.
Systems Thinking: Ability to make architecture trade-offs considering latency, cost, reliability, and maintainability at scale.
Good to Have
Experience with Langraph, Langsmith,and Langchain ecosystem or other agentic libraries like llamaindex, and Google agent kit, crew ai
Large-Scale Systems: Experience with large scale systems, multi-region deployments, and distributed training (DeepSpeed, FSDP).
Advanced RAG & Model Training: Implementation of query routing, text embeddings, instruction tuning, RLHF, or DPO for domain adaptation.
Document Analysis & Multi-Modal AI: Understanding of document analysis and experience with vision-language models (GPT-4V, Claude 3) or document layout analysis for text, table, and image extraction.
Cost Optimization & Research: Track record of reducing inference costs through caching, prompt compression, or model distillation.
What's Happening in AI at SpotDraft
Hyperscale AI Infrastructure: Processing 10B+ tokens per day per region across 4 global data centers with multi-provider orchestration and sub-second failover.
Enterprise-Grade Innovation: Selected as one of 3 startups for Google for Startups Gemini Founders Forum, showcasing cutting-edge foundation model applications.
Edge AI & Privacy: Pioneering on-device AI deployment with Qualcomm for air-gapped, private contract analysis with zero data exfiltration.
Production Reliability:100,000+ contract documents processed per month through AI pipelines with 100% reliability and zero failures.
Industry Recognition: Featured in Fast Company's Most Innovative Companies 2024 (#2 Asia-Pacific) and Forbes Asia's 100 To Watch 2024.
Growth & Funding: $82M+ raised including $56M Series B (Feb 2025), scaling from 170 to 250+ employees serving 400+ enterprise customers.
Why SpotDraft?
Brilliant teammates—Work with some of the sharpest minds in legal tech.
Expand your network—Interact with top founders, investors, and industry leaders.
Real impact—Take ownership of projects and see your work in action.
Big goals, bold moves—We trust you to deliver, innovate, and push boundaries.
Our Core Values
Our business is to delight Customers
Be Transparent. Be Direct
Be Audacious
Outcomes over everything else
Elevate each other
Be Passionate. Take Ownership.
Be 1% better every day
All candidates’ personal data shared during the recruitment process will be handled with utmost confidentiality and used solely for hiring purposes, in line with applicable data protection regulations.
*SpotDraft is an equal-opportunity employer. Candidates will not be discriminated against based on race, ethnicity, color, religion, caste, sex, gender identity, sexual orientation, national origin, veteran, or disability status