AI/ML Engineer (SD1/SD2)

Location: Remote / Bangalore
Type: Full-time
Team: AI/ML Engineering

Disseqt AI helps enterprises build safe, reliable, and compliant AI systems. We develop cutting-edge evaluation, safety, and monitoring capabilities that ensure AI applications behave consistently, securely, and responsibly in production environments.

We are building the next-generation AI safety stack — combining machine learning, smart evaluation systems, and scalable engineering. If you love working at the intersection of ML models, systems engineering, and real-world AI reliability, this role is for you.

We’re looking for an AI/ML Engineer (SD1/SD2) who can design, train, and deploy machine learning models and build production-quality ML services.
You’ll work across model development, evaluation pipelines, and backend integration to support scalable AI systems used by enterprise customers.

This role sits at the heart of our ML platform — combining applied machine learning, NLP, model optimization, and service engineering.

• Build and optimize ML models for text classification, semantic scoring, and evaluation tasks
  

• Develop hybrid ML pipelines combining classical ML, embeddings, and rule-based techniques
  

• Work with transformers, embeddings, and lightweight inference-optimized architectures
  

• Benchmark multiple models for accuracy, latency, and reliability
  

• Build containerized ML services (Python/FastAPI) for high-performance inference
  

• Optimize models for CPU-only execution and on-prem deployment
  

• Implement scalable inference pipelines and internal APIs for model serving
  

• Ensure reliability, uptime, and versioning of ML services
  

• Develop automated evaluation pipelines for model performance and quality
  

• Instrument ML services with tracing, logging, and metrics
  

• Work closely with backend engineers to integrate ML outputs into platform workflows
  

• Train and refine models for text safety, classification, detection, and quality scoring
  

• Build logic to combine statistical, ML, and heuristic signals
  

• Contribute to robust testing frameworks and red-teaming workflows
  

• Work with platform, backend, and infra teams to deploy ML workloads
  

• Write clean, maintainable code and contribute to internal tooling
  

• Participate in code reviews, architecture discussions, and sprint planning
  

• Strong proficiency in Python for ML development
  

• Experience with ML model training and classical ML (SVM, Logistic Regression, XGBoost, etc.)
  

• Experience with NLP libraries (Transformers, spaCy, sentence-transformers)
  

• Familiarity with model evaluation, metrics, and benchmarking
  

• Ability to build APIs/services for ML inference (FastAPI/Flask)
  

• Understanding of containerized deployment (Docker)
  

• Comfortable working with data preprocessing, dataset creation, and experimentation
  

• Experience optimizing models for CPU or resource-constrained environments
  

• Knowledge of embeddings, similarity scoring, and semantic search
  

• Exposure to ML observability, logging, or tracing systems
  

• Experience with rule-based + ML hybrid systems
  

• Hands-on experience with Redis, PostgreSQL
  

• Basic understanding of Golang is a plus
  

• Exposure to agentic/LLM systems, evaluation frameworks, or safety research
  

• Engineers who enjoy owning ML problems end-to-end — model → API → deployment
  

• Those who love practical ML, not just research
  

• Builders who like shipping to production, not just prototyping
  

• People who enjoy working in fast-paced, deeply technical teams
  

• Those passionate about the reliability and governability of AI systems
  

• Work on high-impact ML problems in a rapidly evolving domain
  

• Build real-world AI systems trusted by enterprise customers
  

• 100% ownership of projects with end-to-end visibility
  

• Solve technically challenging ML problems with a top-tier engineering team
  

• Competitive salary + equity