Requirements:
5+ years of hands-on experience in AI/ML engineering, or data science with strong engineering ownership.
Strong Python and solid software fundamentals with clean, well-tested, production-quality code.
Hands-on experience with GenAI/LLMs: prompt design, embeddings, fine-tuning and RAG patterns; experience building agents is a strong advantage.
Experience with core ML; NLP/CV/multimodal experience is a plus.
Familiarity with modern AI toolkits (e.g., LangChain, Hugging Face, PyTorch).
Cloud experience (AWS/GCP/Azure) and working with scalable data pipelines.
Strong communication and collaboration skills.
BS/MS/PhD degree in CS/Data Science/Engineering (MS/PhD a plus).

Requirements:
Technical Expertise:
Programming: Advanced proficiency in Python for backend development and AI applications
GenAI Mastery: Deep understanding of large language models (LLMs) and experience with major model APIs (OpenAI, Anthropic, Google, etc.)
Multi-Agent Systems: Expertise in designing and implementing GenAI multi-agent architectures
Prompt Engineering: Advanced skills in prompt design, optimization, and engineering techniques
Cloud Platforms:
Required: Hands-on experience with AI services in at least one major cloud platform (GCP, Azure, or AWS)
Advantage: Experience across multiple cloud platforms (AI Search, Vertex AI, SageMaker, etc.)
Development Frameworks: Experience with GenAI frameworks like LangChain and cloud-based retrieval services
Software Engineering: Strong background in microservices architecture, API development, and production system design
AI/ML Fundamentals: Solid understanding of deep learning principles and GenAI techniques
Containerization (Advantage): Experience with Docker and Kubernetes for deployment and orchestration
OCR Technologies (Advantage): Experience with Optical Character Recognition systems and document processing
Data Pipelines (Advantage): Experience building and maintaining data processing pipelines
Professional Experience
Mid+ Level Experience: 2+ years in AI/ML development with significant GenAI project experience
Production Systems: Proven track record of deploying and maintaining AI solutions in production environments
Client-Facing Experience: Comfortable with technical presentations and requirement gathering sessions
Education & Background
Preferred: Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or related technical field
Alternative: Demonstrated industrial experience in developing deep learning and GenAI solutions (degree not required with strong portfolio)
Soft Skills
Problem-Solving: Excellent analytical and creative problem-solving abilities
Communication: Strong technical communication skills for both technical and non-technical audiences
Collaboration: Proven ability to work effectively in cross-functional teams
Adaptability: Thrives in fast-paced environments and eager to learn emerging technologies
Consulting Mindset: Ability to understand client needs and provide strategic technical guidance.

Requirements:
What youll need:
Excellent coding skills in Python/TypeScript, with at least 5 years of hands-on experience building reliable backend services, agents and tooling. Familiarity with modern ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face) is a strong advantage.
Experience designing, deploying, and maintaining production systems that integrate ML components, including APIs, microservices, model serving layers, feature pipelines, monitoring, and CI/CD/MLOps workflows.
Solid experience with AI related contexts Understanding of prompt engineering and LLM optimization techniques, RAG architecture
Solid understanding of distributed systems concepts, performance optimization, observability, and operating services at scale.
Strong communication skills, with the ability to bridge technical, product, and business perspectives.
Prior experience in cybersecurity, fraud prevention, or identity management is a plus, especially with secure system architectures or ML-augmented decisioning systems.
Advantages:
Experience integrating with LLM APIs (OpenAI, Anthropic Claude, etc.)
Experience with agent frameworks (LangChain, LlamaIndex, AutoGPT)
Background in ML/AI concepts and model deployment
Experience with message queues (RabbitMQ, Kafka) and event-driven architectures
Experience with function calling and tool use patterns in LLMs.

Requirements:
8+ years industry experience
2+ years industry experience bringing AI models from modeling to production
Expertise and experience in data mining algorithms and statistical modeling techniques such as classification, regression, clustering, anomaly detection, and text mining
Strong understanding of the Software design and architecture process
Experienced with working in cloud production-grade high-scale microservices environment
Languages such as Python & Java
Building and maintaining AI based applications at scale in production
Experience with agentic systems or multi-agent orchestration in AI workflows and AI observability practices.
Exposure to Knowledge Graphs, RAG (Retrieval-Augmented Generation), or semantic search.
Understanding of AI infrastructure components, including the prompt lifecycle, fallback logic, and feature-level configuration.
Excellent oral and written English communication skills: demonstrated ability to explain complex technical issues to both technical and non-technical audiences
BS, MS in an appropriate technology field (Computer Science, Statistics, Applied Math, Operations Research), or equivalent work experience
Advantage:
Data science model training:
Well versed in Data Science languages, tools and frameworks, including data processing platforms and distributed computing systems (for example Python, R, SQL, SKLearn, NumPy, Pandas, TensorFlow, Keras)
Familiarity with vector database
Machine Learning experience.

Requirements:
Expreince leading engineering teams in fast-paced environments with strong ownership and architectural responsibility.
Backend Expertise: 6+ years of backend development experience with deep expertise in Node.js and microservices. Strong distributed systems and API design experience.
GenAI Systems Experience: Hands-on experience building production LLM systems. Proven experience with RAG architectures, vector databases, embedding pipelines, and prompt orchestration. Experience designing multi-step or agentic AI workflows.
Infrastructure: Strong experience with AWS and Kubernetes in production environments. Deep knowledge of SQL & NoSQL systems.
Communication: Ability to translate complex AI systems into clear product and business decisions.

Requirements:
Masters/Ph.D. in Computer Science, Electrical Engineering, Machine Learning, information systems engineering, or related field.
5+ years of hands-on experience with developing & maintaining production class Machine Learning projects aimed towards solving business problems.
High proficiency in Python
Hands-on proficiency with modern generative AI models, prompt engineering, multi-agents, and RAG architectures. Also, a solid understanding of transformers, optimized fine-tuning techniques, and evaluation methodologies of LLMs while using relevant frameworks (Hugging Face, LangChain, LlamaIndex, etc)
Familiarity with embedding models and vector databases - advantage
Experience in data exploration and visualization

Requirements:
5+ years of professional backend engineering experience.
Strong proficiency in at least one backend programming language (Java etc).
Experience building and maintaining production-grade APIs and microservices.
Solid understanding of distributed systems, data structures, and algorithms.
Experience with SQL and NoSQL databases.
Track record of delivering consumer-facing products at scale.
Experience with AI/ML platforms and services (OpenAI, Hugging Face, etc.)
Familiarity with LLMs, RAG, or conversational AI.
Knowledge of vector databases and embedding technologies.
Experience with cloud platforms (AWS, GCP, Azure).
Understanding of AI/ML concepts and implementation patterns.

Requirements:
Requirements:

3+ years of hands-on experience in software engineering, with at least 1-2 years working on generative AI projects (LLMs, diffusion models, multimodal models, etc.).

Proven ability to go from idea to production-ideally with examples of real-world AI features youve shipped.

Fluency in Python, Node.js, or similar languages used in ML and full-stack development.

Experience with prompt engineering, fine-tuning, or embedding models using frameworks like LangChain, LlamaIndex, or similar.

Familiarity with AWS services and best practices, including Lambda, S3, SageMaker, ECS/EKS, Bedrock etc.

Experience with MLOps and model deployment practices (e.g., containerization, GPU inference, vector databases).

Creativity and initiative-able to pitch and prototype ideas with minimal oversight.

Strong communication skills and the ability to explain technical concepts to non-technical stakeholders.

Nice-to-Haves:

Prior experience integrating generative AI in FinOps or cloud cost optimization tools.

Background in NLP, computer vision, or other relevant ML fields.

Contributions to open-source AI tools or research.

Knowledge of responsible AI principles and handling model risks.

Requirements:
8+ years of software engineering experience, with at least 2+ years in AI/ML platforms or intelligent automation.
Strong background in distributed systems, APIs, microservices, container orchestration (ECS/EKS), and cloud platforms (AWS/GCP/Azure)
Proven experience building and deploying LLM-based applications using platforms such as OpenAI, Anthropic, AWS Bedrock, or similar
Solid understanding of RAG pipelines, vector databases, embeddings, and retrieval techniques (chunking, indexing, filtering, relevance tuning)
Ability to thrive in ambiguous problem spaces, take ownership, and move quickly in a startup-like environment
Strong problem-solving and analytical skills, with a product- and impact-driven mindset
Experience collaborating across teams and leading technical projects end-to-end
Bachelors degree in Computer Science, AI, or a related field (or equivalent practical experience)

Requirements:
7+ years of experience in data infra or backend engineering.
Strong knowledge of data services architecture, and ML Ops.
Experience with cloud-based data infrastructure in the cloud, such as AWS, GCP, or Azure.
Deep experience with SQL and NoSQL databases.
Experience with Data Warehouse technologies such as Snowflake and Databricks.
Proficiency in backend programming languages like Python, NodeJS, or an equivalent.
Proven leadership experience, including mentoring engineers and driving technical initiatives.
Strong communication, collaboration, and stakeholder management skills.
Bonus Points:
Experience leading teams working with serverless technologies like AWS Lambda.
Hands-on experience with TypeScript in backend environments.
Familiarity with Large Language Models (LLMs) and AI infrastructure.
Experience building infrastructure for Data Science and Machine Learning.
Experience collaborating with BI developers and analysts to drive business value.
Expertise in administering and managing Databricks clusters.
Experience with streaming technologies such as Amazon Kinesis and Apache Kafka.