דרושים » הנדסה » AI Developer

Requirements:
B.Sc. or higher in Computer Science or related field.
6+ years of full-stack experience in fast-paced startup environments.
Proficient in TypeScript/JavaScript, React, and Node.js (or Python); familiar with AWS.
Experience with LLM APIs and agent frameworks (e.g., LangChain, AutoGPT, CrewAI).
Hands-on with GenAI models (OpenAI, Claude, Gemini, or open-source like LLaMA).
Strong product sense and user-focused mindset.
Excellent English - written and spoken.

Requirements:
3+ years of experience delivering production-grade ML/AI systems
Strong Python skills and solid understanding of the ML lifecycle
Experience with GPU infrastructure, containerization (Docker) and cloud platforms
Familiarity with microservice architectures and API development
Hands-on experience with LLM pipelines and agent orchestration frameworks (LangGraph, LlamaIndex, etc.)
Knowledge of experiment tracking tools (Weights & Biases, MLflow, or similar)
Background in scalable ML infrastructure, distributed computing, and workflow orchestration frameworks (Ray, Kubeflow, Airflow)
Experience with multi-node training (advantage)
Collaborative mindset with startup-level ownership and pragmatism

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)
You might also have
Experience with AI evaluation frameworks, prompt testing, or offline/online quality measurement
Familiarity with MLOps or AI observability tooling
Interest in gaming, mobile products, or large-scale consumer platforms

Requirements:
Bachelors degree in computer science, Engineering, or a related field, or equivalent practical experience.
At least 5 years of professional experience as a software engineer building and operating production systems.
Experience in designing and deploying end-to-end GenAI solutions.
Solid understanding of software engineering fundamentals, including data structures, algorithms, testing, and version control.
Ability to collaborate effectively across teams and communicate complex technical concepts clearly.
Experience building cloud-based services on platforms such as Azure, AWS, or GCP.
Preferred
Master's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
Hands-on experience designing and building solutions using AI/LLM frameworks and tooling (e.g., Semantic Kernel, LangChain, vector databases).
Knowledge of secure software development practices and threat modeling.
Experience applying responsible AI principles, including safety, evaluation, and governance.
Knowledge of the cybersecurity domain is a plus, including familiarity with security threats, attack vectors, detection and response concepts, and modern security solutions.

Requirements:
2+ years of hands-on experience designing, training, and deploying machine-learning models
M.Sc. or higher in Computer Science / Mathematics / Statistics or equivalent from a university, or B.Sc. with strong hands-on ML experience
Practical experience with Natural Language Processing (NLP) and LLMs
Experience with data acquisition, data cleaning, and data pipelines
A passion for building products and helping people, both customers and colleagues
All-around team player, fast, self-learning individual
Nice to have:
3+ years of development experience with a passion for excellence
Experience building AI coding assistants, code reasoning models, or dev-focused LLM agents.
Familiarity with RAG, function-calling, and tool-using LLMs.
Knowledge of model optimizations (quantization, distillation, LoRA, pruning).
Startup or product-driven ML experience, especially in high-scale, latency-sensitive environments.
Contributions to open-source AI or developer tools.

Requirements:
3+ years in roles such as, Business Application Developer, Business Analyst, Product Operations, RevOps, AI/ML Ops, or Digital Transformation in a global SaaS / data-heavy company.
Proven experience designing and delivering AI-driven workflows or agents in a SaaS environment, partnering closely with business stakeholders.
Low-code/no-code build skills and strong prompt engineering fundamentals (tool/function calling, structured/JSON outputs, context windows, grounding).
Hands-on experience with AI Gateway-style action catalogs (configure inputs/outputs, field mapping, chaining, retries, error handling), and familiarity with platforms like Salesforce, Snowflake, Gong, ERP, ticketing, HRIS, etc.
Solid SQL skills for analysis, shaping data for agents, defining evaluation sets, and measuring workflow performance (no need to be a full data engineer).
Familiarity with Context Engineering using APIs, MCPs, retrieval, and data contracts.
Understanding of hallucination-mitigation strategies (retrieval grounding, instruction hierarchy, eval sets, guardrails, refusals).
Systems thinker who can map complex ecosystems (agents, tools, data sources, owners) and make them understandable.
High data literacy: comfortable with usage data exploration, defining metrics, and building dashboards with Data/BI teams.
Strong stakeholder management: able to work with executives on value, Security/IT on controls, Data/Engineering on telemetry/models, and business teams on workflows.
Outcome-driven: bias to measurable impact (time saved, error reduction, revenue influence) over vanity metrics.
Clear, concise communication and documentation skills.
Nice to have
Experience launching organization-wide AI/automation programs or agent governance.
Background in observability/telemetry for LLM systems (cost tracking, evals, quality signals).
Prior work with risk/compliance programs (data classification, RBAC, policy reviews).
Built enablement programs (docs, workshops, champions communities) that changed behavior.
Familiarity with offline/online experimentation, prompt/agent evaluation, and KPI design for AI workflows.

Requirements:
3-5 years of experience in ML Engineering, AI models deployment or MLOps roles.
Strong software engineering background with hands-on experience (Python or Java preferred) building and maintaining production ML services
Solid understanding of machine learning systems and inference pipelines
Familiarity with monitoring practices and production diagnostics for ML services (e.g logs, metrics, alerting)
Proven experience in optimizing AI models for performance (response-time, memory, CPU usage) particularly in real-time or large-scale environments.
Strong proficiency with ML frameworks (TensorFlow, PyTorch, Scikit-Learn, etc.)
Experience deploying AI solutions in cloud environments (AWS, GCP, or Azure)
Nice to have
Familiarity with GenAI production environments - working with LLMs, vector databases, and third-party generative AI APIs.
Understanding of AI governance, compliance, security, and responsible AI principles.

Requirements:
Advanced knowledge and experience in NLP and use of LLMs for GenAI applications in production at scale.
Strong experience in designing end-to-end R&D plans for GenAI including evaluation and validation lifecycle and benchmarking.
Strong proficiency in Python
Solid understanding of Data Science and Machine Learning lifecycle and best practices evaluating and validating AI systems at scale.
Excellent problem-solving abilities, coupled with a creative and strategic mindset.
Proven ability to work effectively in a team setting.
Advantages:
Experience with EDD (evaluation driven development) for GenAI applications.
Familiarity with cybersecurity applications of GenAI.
Advanced skills in performance optimization for high throughput systems.
Tech Stack:
Python, Langchain, Langgraph (or other agentic frameworks), Langfuse/LangSmith (or other observability and tracing tools), HuggingFace, Mlflow, MongoDB

Requirements:
6+ years building production-grade backend or ML systems that real users depend on
Tangible experience with LLMs prompting, fine-tuning, or deploying them behind an API (show us what you've shipped)
Fluency in at least one backend language (TypeScript, Python) and familiarity with cloud-native infra (Docker, K8s, AWS)
Strong grounding in algorithms, data structures, and probability CS, EE, Math, or equivalent real-world chops
Demonstrated skill in building resilient, high-traffic, "can't-go-down" services (finance, gaming, ad-tech, etc.)
Passion for agentic coding: chaining tools, orchestrating workflows, letting the machine do the grunt work
Curiosity, pragmatism, and the ability to learn crazy-fast (zero bureaucracy, maximum building)
Hands-on with vector databases (Pinecone, Weaviate, PGVector) or semantic search systems - an advantage
Experience implementing policy, safety, or red-teaming frameworks around GenAI - an advantage
Open-source contributions or published research in NLP / generative modeling - an advantage
Prior work with voice bots, multimodal models, or real-time streaming architectures - an advantage
Ability to work in an office environment a minimum of 3 days a week.

Requirements:
6+ years of experience as a Backend Developer, Data Engineer, or ML Engineer
Bachelors degree in Computer Science or a related STEM field
Strong proficiency in Python and ML tooling
Proven ability to build production-grade ML systems end-to-end
Deep experience with LLMs and ML frameworks (e.g., LangChain, LangGraph, Hugging Face, TensorFlow, PyTorch)
Solid foundation in system design, architecture, and microservice patterns
Excellent problem-solving skills and ownership mindset
Strong collaboration and communication abilities
Bonus if you have:
M.Sc. in Computer Science, Software Engineering, or similar field
Experience building and scaling LLM-powered applications
Familiarity with AWS and DevOps best practices (CI/CD, monitoring, IaC)
Exposure to NoSQL and real-time data processing pipelines