
AI/ML Engineer
3 weeks ago
Exp : 6+ years
Location : Gurgoan
Job Type : 6 months contract + ext.
1. Core Responsibilities :
Design, develop, and optimize ML systems :
- Build state-of-the-art machine learning models - especially large language models (LLMs) or multimodal models - and continuously enhance performance. Draws from roles like Model- Implement model behavior evaluation frameworks : design, test, and debug experimental pipelines in both research and product contexts
Infrastructure & Systems Engineering :
- Tune system performance, manage resource utilization (GPU/CPU/memory), profile system behavior, and deploy optimization tooling
ML Infrastructure & Data Platforms :
- Design and manage data pipelines, orchestration (e.g., Kafka, Spark, Airflow), and large-scale storage systems (Iceberg, Delta) to enable ML workflows
- Craft and maintain data ingestion, streaming, feature engineering, and operational reliability practices.
Model Deployment & Monitoring :
- Integrate models into production, ensuring they are robust, performant, and reliable in real-world usage- Build telemetry and failure-analysis systems to detect model drift, calibration issues, or adversarial impacts
- Support on-call rotations and incident response for critical production Collaboration & Mentorship :
- Work alongside ML researchers, software engineers, hardware teams, and product groups to translate AI research into scalable, user-safe implementations
- Mentor junior engineers, conduct code reviews, and lead by example in building reliable ML systems
2. Key Qualifications & Skills Proficiency :
- Strong coding skills in languages such as Python, C++, and sometimes Rust
- Deep experience with ML frameworks (e.g., PyTorch, TensorFlow), and familiarity with LLMs or multimodal models
- Hands-on knowledge with GPU programming (CUDA, kernel optimization), performance profiling, or architectures like HBM, warp utilization, low-precision formats
Systems & Infrastructure Mastery :
- Expertise in distributed systems, cloud environments, container orchestration (e.g., Kubernetes), performance tools (Nsight, perf), and infrastructure-as-code tools (Terraform)- Experience designing simulation frameworks, benchmarking tools, or architectural modeling stacks
ML Engineering Foundations :
- Background in computer vision, audio ML, or real-time systems for authentication or perception, when relevant
Soft Skills :
- Strong analytical thinking, problem-solving, and ability to work with ambiguous or loosely defined challenges
- Proactive communicator, able to collaborate efficiently across diverse teams, and resilient in fast-paced environments
(ref:hirist.tech)-
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